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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Soil-related developments of the Biome-BGCMuSo v6.2 terrestrial ecosystem model

Terrestrial biogeochemical models are essential tools to quantify climate–carbon cycle feedback and plant–soil relations from local to global scale. In this study, a theoretical basis is provided for the latest version of the Biome-BGCMuSo biogeochemical model (version 6.2). Biome-BGCMuSo is a branch of the original Biome-BGC model with a large number of developments and structural changes. Earlier model versions performed poorly in terms of soil water content (SWC) dynamics in different environments. Moreover, lack of detailed nitrogen cycle representation was a major limitation of the model. Since problems associated with these internal drivers might influence the final results and parameter estimation, additional structural improvements were necessary. In this paper the improved soil hydrology as well as the soil carbon and nitrogen cycle calculation methods are described in detail. Capabilities of the Biome-BGCMuSo v6.2 model are demonstrated via case studies focusing on soil hydrology, soil nitrogen cycle, and soil organic carbon content estimation. Soil-hydrology-related results are compared to observation data from an experimental lysimeter station. The results indicate improved performance for Biome-BGCMuSo v6.2 compared to v4.0 (explained variance increased from 0.121 to 0.8 for SWC and from 0.084 to 0.46 for soil evaporation; bias changed from -0.047 to -0.007 m 3 m -3 for SWC and from -0.68 to -0.2 mm d -1 for soil evaporation). Simulations related to nitrogen balance and soil CO 2 efflux were evaluated based on observations made in a long-term field experiment under crop rotation. The results indicated that the model is able to provide realistic nitrate content estimation for the topsoil. Soil nitrous oxide (N 2 O) efflux and soil respiration simulations were also realistic, with overall correspondence with the observations (for the N 2 O efflux simulation bias was between -0.13 and -0.1 mgNm -2 d -1 , and normalized root mean squared error (NRMSE) was 32.4 %–37.6 %; for CO 2 efflux simulations bias was 0.04–0.17 gCm -2 d -1 , while NRMSE was 34.1 %–40.1 %). Sensitivity analysis and optimization of the decomposition scheme are presented to support practical application of the model. The improved version of Biome-BGCMuSo has the ability to provide more realistic soil hydrology representation as well as nitrification and denitrification process estimation, which represents a major milestone.

58 GEOSCIENCES↗
NASA NTRS2010

Diurnal and Directional Responses of Chlorophyll Fluorescence and the PRI in a Cornfield

Determining the health and vigor of vegetation using high spectral resolution remote sensing is an important goal which has application to monitoring agriculture and ecosystem productivity and carbon exchange. Two spectral indices used to assess whether vegetation is performing near-optimally or exhibiting symptoms of environmental stress (e.g., drought or nutrient deficiency, non-optimal temperatures, etc.) are the Photochemical Reflectance Index (PRI) and solar-induced red and far-red Chlorophyll Fluorescence (Fs). Both the PRI and Fs capture the dynamics of photoprotection mechanisms within green foliage: the PRI is based on the association of the reflected radiation in the green spectrum with the xanthophyll cycle, whereas Fs measures the emitted radiation in the red and far-red spectrum. Fs was determined from retrievals in the atmospheric oxygen absorption features centered at 688 and 760 nm using a modified Fraunhofer Line Depth (FLD) method. We previously demonstrated diurnal and seasonal PRI differences for sunlit vs. shaded foliage in a conifer forest canopy, as expressed in the hotspot and darkspot of the Bidirectional Reflectance Function (BRF). In a USDA-ARS experimental field site located in Beltsville, MD, USA, measurements were acquired over a corn crop from a nadir view in 2008 with an ASD FieldSpec Pro (Analytical Spectral Devices, Inc., Boulder, CO, USA) to study the behavior of the PRI for sunlit and shaded foliage as captured in reflectance variations associated with the BRF, in a I m tall canopy in the vegetative growth stage. Those observations were compared to simulations obtained from two radiative transfer models. Measurements were then acquired to examine whether the PRI and Fs were influenced by view zenith and azimuth geometries at different times of day. Those measurements were made in 2010 with the Ocean Optics USB4000 Miniature Fiber Optic Spectrometer (Ocean Optics Inc., Dunedin, Florida, USA) at several times during the day on multiple days throughout the growing season. We found that the PRI consistently had higher values, indicating lower stress, in the BRF darkspot associated with shaded foliage than in the hotspot associated with sunlit foliage. We also found that Fs exhibited differences associated with sunlit and shaded canopy sectors, which were most pronounced for the red/far-red Fs ratio. Values indicated greater physiological stress in afternoons compared to mornings, and in the early senescent canopy as compared to the vegetative growth stage, BRFs for both the PRI and the red/far-red Fs ratio were bowl-shaped for the full azimuth sweep of the canopy. These two spectral indices (PRI, Fs ratio) provided complementary information on the photosynthetic function of the corn canopy.

Middleton, Elizabeth↗
NASA NTRSDate not supplied

Characterizing the Impact of Equatorial Plasma Bubbles on GNSS Accuracy for Crop Yield Estimation in Brazil with NASA GOLD Data

In Brazil, equatorial plasma bubbles are the primary space weather phenomenon impacting the performance of Global Navigation Satellite Systems (GNSS), causing signal disruptions and positional inaccuracies in agricultural machinery used for mapping fields, guiding machinery, and ensuring accurate planting, spraying, and harvesting. As a result, farmers and stakeholders face challenges in maintaining accurate operational capabilities, often needing to shut down machinery to mitigate these effects. This project evaluated the feasibility of using NASA’s Heliophysics observations for a case study in March 2024. Data from NASA's Global-scale Observations of the Limb and Disk (GOLD) instrument were used to characterize equatorial plasma bubbles, while amplitude and phase indices from ground-based GNSS receivers confirmed their presence and impact on signal disruptions. Additionally, data from a local farm's rover receiver assessed the practical implications of these disruptions on Real-Time Kinematic (RTK) GNSS-based operations by calculating the 2-dimensional error. The project involved collaboration across multiple different organizations and sectors, including NASA’s Crustal Dynamics Data Information System (CDDIS) researchers, John Deere, a Brazilian farm for data acquisition, and Brazilian universities for data interpretation. The results aim to inform Brazilian farmers about position errors affecting their precision agriculture operations and provide insights into the needs of space weather data users and modeling. Lessons from this case study will guide future integration of space weather data and modeling with end users.

Melissa Marquez↗
NASA NTRS2012

The Aerosol-Monsoon Climate System of Asia

In Asian monsoon countries such as China and India, human health and safety problems caused by air-pollution are worsening due to the increased loading of atmospheric pollutants stemming from rising energy demand associated with the rapid pace of industrialization and modernization. Meanwhile, uneven distribution of monsoon rain associated with flash flood or prolonged drought, has caused major loss of human lives, and damages in crop and properties with devastating societal impacts on Asian countries. Historically, air-pollution and monsoon research are treated as separate problems. However a growing number of recent studies have suggested that the two problems may be intrinsically intertwined and need to be studied jointly. Because of complexity of the dynamics of the monsoon systems, aerosol impacts on monsoons and vice versa must be studied and understood in the context of aerosol forcing in relationship to changes in fundamental driving forces of the monsoon climate system (e.g. sea surface temperature, land-sea contrast etc.) on time scales from intraseasonal variability (~weeks) to climate change (~ multi-decades). Indeed, because of the large contributions of aerosols to the global and regional energy balance of the atmosphere and earth surface, and possible effects of the microphysics of clouds and precipitation, a better understanding of the response to climate change in Asian monsoon regions requires that aerosols be considered as an integral component of a fully coupled aerosol-monsoon system on all time scales. In this paper, using observations and results from climate modeling, we will discuss the coherent variability of the coupled aerosol-monsoon climate system in South Asia and East Asia, including aerosol distribution and types, with respect to rainfall, moisture, winds, land-sea thermal contrast, heat sources and sink distributions in the atmosphere in seasonal, interannual to climate change time scales. We will show examples of how elevated absorbing aerosols (dust and black carbon) may interact with monsoon dynamics to produce feedback effects on the atmospheric water cycle, leading to in accelerated melting of snowpacks over the Himalayas and Tibetan Plateau, and subsequent changes in evolution of the pre-monsoon and peak monsoon rainfall, moisture and wind distributions in South Asia and East Asia.

Lau, William K. M.↗
DOE OSTI2020

The Utility of Satellites and Autonomous Remote Sensing Platforms for Monitoring Offshore Aquaculture Farms: A Case Study for Canopy Forming Kelps

The emerging sector of offshore kelp aquaculture represents an opportunity to produce biofuel feedstock to help meet growing energy demand. Giant kelp represents an attractive aquaculture crop due to its rapid growth and production, however precision farming over large scales is required to make this crop economically viable. These demands necessitate high frequency monitoring to ensure outplant success, maximum production, and optimum quality of harvested biomass, while the long distance from shore and large necessary scales of production makes in person monitoring impractical. Remote sensing offers a practical monitoring solution and nascent imaging technologies could be leveraged to provide daily products of the kelp canopy and subsurface structures over unprecedented spatial scales. Here, we evaluate the efficacy of remote sensing from satellites and aerial and underwater autonomous vehicles as potential monitoring platforms for offshore kelp aquaculture farms. Decadal-scale analyses of the Southern California Bight showed that high offshore summertime cloud cover restricts the ability of satellite sensors to provide high frequency direct monitoring of these farms. By contrast, daily monitoring of offshore farms using sensors mounted to aerial and underwater drones seems promising. Small Unoccupied Aircraft Systems (sUAS) carrying lightweight optical sensors can provide estimates of canopy area, density, and tissue nitrogen content on the time and space scales necessary for observing changes in this highly dynamic species. Underwater color imagery can be rapidly classified using deep learning models to identify kelp outplants on a longline farm and high acoustic returns of kelp pneumatocysts from side scan sonar imagery signal an ability to monitor the subsurface development of kelp fronds. Current sensing technologies can be used to develop additional machine learning and spectral algorithms to monitor outplant health and canopy macromolecular content, however future developments in vehicle and infrastructure technologies are necessary to reduce costs and transcend operational limitations for continuous deployment in an offshore setting.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Life Cycle Analysis of Growing Canola for Biofuel Production in the United States

This study quantifies and compares the life cycle greenhouse gas (GHG) emissions of renewable diesel (RD), sustainable aviation fuel (SAF), and biodiesel (BD) produced from two U.S. canola production systems: 1) emerging intermediate winter canola, typically grown in double- or relay-cropping systems between the growing seasons of main crops, and 2) main canola, mostly spring canola but also including winter canola, which are grown as primary crops occupying the field for a full growing season. Using the Research and Development version of the Greenhouse gases, Regulated Emissions, and Energy use in Technologies (R&D GREET) model and the most up-to-date life cycle inventory data─field trial data for intermediate winter canola (>37,000 acres) and recent national survey data for spring canola─this life cycle analysis (LCA) estimates the direct emissions from canola cultivation and harvest, the conversion of canola into fuels, fuel transportation, and combustion. In addition, we account for market-mediated emissions associated with a scenario of 0.5 billion gallons per year of spring canola-based biofuels, including induced land use change (ILUC), induced other crop (nonfeedstock) production changes, and induced livestock production changes. For intermediate winter canola, these market-mediated effects were not modeled, as ILUC is expected to be negligible due to its integration into existing rotations, and data are currently insufficient to reliably quantify other market-mediated changes. The estimated life cycle direct emissions of RD/SAF derived from intermediate winter canola and main spring canola are about 32 and 33 g of CO2-equivalent per megajoule of fuel (g CO 2 e/MJ), respectively. Corresponding emissions for BD from intermediate winter canola and main spring canola are about 30 and 31 g of CO 2 e/MJ, respectively. Farming is the dominant emissions source for both canola systems, with intermediate winter canola and main spring canola emitting about 19 and 20 g of CO 2 e/MJ, respectively. ILUC and other induced changes increase emissions of main spring canola-derived RD/SAF and BD by about 18 and 17 g of CO 2 e/MJ, respectively. These results indicate that the GHG emissions of biofuels produced from the two canola systems may differ substantially due to the different land use dynamics of the systems.

biodiesel↗
DOE OSTI2022

Representing Global Soil Erosion and Sediment Flux in Earth System Models

Abstract Soil erosion produces enormous amounts of sediment, carbon, and nutrient fluxes from land to rivers, thus playing crucial roles in global biogeochemical cycles and food security. To predict soil erosion in the context of climate and land use changes, we explicitly parameterize cropland management actions (i.e., irrigation, conserved agriculture, and crop residue management) and geological factors (i.e., lithology and glacier) in the Energy Exascale Earth System Model (E3SM) soil erosion module. The erosion model is calibrated using a global‐scale regionalized parameter calibration method. The spatial variabilities of the modeled and the Revised Universal Soil Loss Equation (RUSLE) based benchmark soil erosion are consistent across vegetation, climate and soil properties. Compared with independent data, our model shows a bias reduction in 59% of the observations relative to the RUSLE‐based soil erosion, with 53% of the bias reduction exceeding 50%. This improvement is mainly due to a better representation of the topographic effect on soil erosion. Our results indicate that conserved agriculture practices have effectively reduced soil erosion in cropland by over 25% in the United States and Argentina. In contrast, irrigation has increased soil erosion in many Asian countries. For upland sediment flux, our model is consistent with the WBMsed benchmark data in inter‐basin variability but could be more skillful in simulating intra‐basin variability because it couples soil erosion and sediment flux explicitly. The developed model provides useful skills for more realistic predictions of soil erosion and river sediment dynamics under environmental changes.

58 GEOSCIENCES↗
DOE OSTI2022

Increased bundle‐sheath leakiness of CO 2 during photosynthetic induction shows a lack of coordination between the C 4 and C 3 cycles

Summary Use of a complete dynamic model of NADP‐malic enzyme C 4 photosynthesis indicated that, during transitions from dark or shade to high light, induction of the C 4 pathway was more rapid than that of C 3 , resulting in a predicted transient increase in bundle‐sheath CO 2 leakiness ( ϕ ). Previously, ϕ has been measured at steady state; here we developed a new method, coupling a tunable diode laser absorption spectroscope with a gas‐exchange system to track ϕ in sorghum and maize through the nonsteady‐state condition of photosynthetic induction. In both species, ϕ showed a transient increase to > 0.35 before declining to a steady state of 0.2 by 1500 s after illumination. Average ϕ was 60% higher than at steady state over the first 600 s of induction and 30% higher over the first 1500 s. The transient increase in ϕ , which was consistent with model prediction, indicated that capacity to assimilate CO 2 into the C 3 cycle in the bundle sheath failed to keep pace with the rate of dicarboxylate delivery by the C 4 cycle. Because nonsteady‐state light conditions are the norm in field canopies, the results suggest that ϕ in these major crops in the field is significantly higher and energy conversion efficiency lower than previous measured values under steady‐state conditions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Assessing the Additional Carbon Savings with Biofuel

A recent study claims that corn used for ethanol should not be considered to be inherently biogenically carbon-neutral because not all that corn was grown additional to the level otherwise. By assessing the extent of carbon neutrality of corn for ethanol using the reference point baseline approach and historical data that study concluded that the carbon intensity of US corn ethanol is 27% higher than that of gasoline. We develop a framework to determine the carbon neutrality of corn for ethanol by assessing the additional carbon uptake by crops using an anticipated baseline approach. We also apply this framework to determine the additional corn produced for ethanol and include the direct life cycle carbon emissions of only that portion of corn in the direct life cycle carbon intensity of corn ethanol. We implement this framework by integrating an economic model of the agricultural sector in the USA with a biogenic carbon model and life cycle analysis to quantify biogenic carbon uptake and direct life cycle emissions with and without corn ethanol expansion over the 2007–2027 period. We find that the combined biogenic carbon emissions and direct life cycle carbon emission intensity of corn ethanol (not including indirect land use related emissions) is 21% lower than gasoline. In conclusion, the lower value of this carbon intensity of corn ethanol compared with gasoline is robust to a wide range of parametric assumptions.

09 BIOMASS FUELS↗
NASA NTRS2007

Canopy Level Solar Induced Fluorescence for Vegetation in Controlled Experiments

Solar induced chlorophyll fluorescence (SIF) was retrieved from high resolution reflectance spectra acquired one meter above saplings of three deciduous tree species during springtime (three weeks after leaf flush) and in late summer when foliage was mature. SIF was determined by application of the Fraunhofer Line Depth (FLD) Principal to above-canopy spectra acquired with an Analytical Spectral Devices (ASD) Fieldspec spectroradiometer (3.2 nm resolution with 1.2 nm sampling interval). SIF retrievals were made at the two atmospheric oxygen (O2) absorption features that occur in the chlorophyll fluorescence (ChlF) region (660 -780 nm). These telluric features are 02V, the broader and deeper feature centered at 760 nm, but located on the shoulder of the far-red ChlF peak at 740 nm; and 023, a narrow feature centered at 688 nm that is positioned near the red ChlF peak at 685 nm. Supporting, coincident leaf level fluorescence, reflectance, photochemical and other measurements were also made. At the leaf level, these measurements included in situ photosynthetic capacity (Pmax) and light adapted total chlorophyll fluorescence (Fs') collected at steady state under high light and controlled chamber conditions (e.g., temperature, PAR, humidity, and COz); optical properties (reflectance, transmittance, absorptance); chlorophyll and carotenoid content; specific leaf mass; carbon (C) and nitrogen (N) content; fluorescence emission spectra at multiple excitation wavelengths; the ChlF contribution to red (R) and far-red (FR) reflectance; fluorescence imagery; and fluorescence excitation-emission matrices (EEMs). The tree species examined were tulip poplar (Liriodendron tulipifera L.), red maple (Acer rubrum L.), and sweetgum (Liquidambar styraczflua L.), and each had been provided four levels of N augmentation (0, 19, 37, and 75 kg Nhectare seasonally) to simulate atmospheric deposition from air pollution. Whole-plant SIF measurements of these species were compared with SIF estimates derived using FluorMOD, a radiative transfer model that includes fluorescence properties of foliage, when provided with our supporting measurements. Simulated values for SIF were also compared with similar estimates made over three years for corn (Zea mays L) crops under N treatments (20, 50,70, and 140 kg Nhectare) and with corn and pepper (Capsicum annuum L.) plants provided dimethyl urea (DCMU, 0 and 5 x 10" M) which were grown in pots and grouped in artificial canopies for spectral measurements. For near-surface measurements of healthy and stressed vegetation, red SIF retrieved at 688 nm (023) varied between 2-7 mW/m(exp 2)/nm/sr while far-red SIF retrieved at 760 nm (O2A) varied between 0.5 and 4.0 mW/m(exp 2)/nm/sr. Typical values for the SIF red/far-red ratio ranged between 1.75 and 4.0. Relationships of SIF to spectral reflectance indices and foliar photochemical indices were examined, such as photosynthetic light use efficiency. Initial investigations of the variability in these measurements at the same leaf temperature for sunlit and shaded foliage showed us that shaded foliage produced higher ChlF and light use efficiency. Other factors that affect SIF determinations are discussed. These results will assist in determine the expected intensity of the SIF signal from vegetation near the surface, as well as to identify enhancements needed for FluorMOD (or other such models), and will assist in determining relationships of SIF to reflectance indices and carbon dynamics.

Middleton, E. M.↗
DOE OSTI2021

Land cover change-induced decline in terrestrial gross primary production over the conterminous United States from 2001 to 2016

As one of the most dynamic aspects of global environmental change, land cover change (LCC) has a profound impact on terrestrial carbon sequestration. However, LCC-induced carbon fluxes are still the most uncertain terms in global and regional carbon budgets. Ecosystem gross primary production (GPP) is the total carbon uptake by vegetation through photosynthesis, serving as a major control on ecosystem function and land carbon balance during and after the modification of the land surface. However, accurately capturing LCC-induced GPP changes requires both high-quality land cover data and controlling for variation driven by other environmental factors such as climate. In this study, we comprehensively examined the effects of LCC on annual GPP trends over the conterminous United States (CONUS) from 2001 to 2016 using the USGS National Land Cover Database, a remote sensing-driven ecosystem model, and the Google Earth Engine cloud computing platform. We designed a series of model experiments to identify LCC effects on GPP by controlling climate effects. During the study period, LCC exerted a strong negative effect on total GPP across the CONUS ([-2.2, -1.8] Tg C yr -2 ), while climate had smaller positive effects ([0.17, 0. 92] Tg C yr -2 ). The LCC-induced reduction of GPP was mainly caused by net forest loss ([-1.98, -1.39] Tg C yr -2 ) and urban expansion ([-2.03, -1.92] Tg C yr -2 ), but was partially offset by increases in crop area ([+0.66, +0.79] Tg C yr -2 ). Ensemble simulations from TRENDY did not capture the strong negative LCC influences on GPP, likely due to limitations of the adopted land use/cover data. Overall, our study provides a novel perspective on LCC-induced GPP changes, which could help to improve our understanding of ecosystem function changes and constrain the estimation of land carbon balance in the context of anthropogenic activity and climate change.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2010

Utilizing In Situ Directional Hyperspectral Measurements to Validate Bio-Indicator Simulations for a Corn Crop Canopy

Two radiative transfer canopy models, SAIL and the two-layer Markov-Chain Canopy Reflectance Model (MCRM), were coupled with in situ leaf optical properties to simulate canopy-level spectral band ratio vegetation indices with the focus on the photochemical reflectance index in a cornfield. In situ hyperspectral measurements were made at both leaf and canopy levels. Leaf optical properties were obtained from both sunlit and shaded leaves. Canopy reflectance was acquired for eight different relative azimuth angles (psi) at three different view zenith angles (Theta (sub v)), and later used to validate model outputs. Field observations of photochemical reflectance index (PRI) for sunlit leaves exhibited lower values than shaded leaves, indicating higher light stress. Canopy PRI expressed obvious sensitivity to viewing geometry, as a function of both Theta (sub v) and psi . Overall, simulations from MCRM exhibited better agreements with in situ values than SAIL. When using only sunlit leaves as input, the MCRM-simulated PRI values showed satisfactory correlation and RMSE, as compared to in situ values. However, the performance of the MCRM model was significantly improved after defining a lower canopy layer comprised of shaded leaves beneath the upper sunlit leaf layer. Four other widely used band ratio vegetation indices were also studied and compared with the PRI results. MCRM simulations were able to generate satisfactory simulations for these other four indices when using only sunlit leaves as input; but unlike PRI, adding shaded leaves did not improve the performance of MCRM. These results support the hypothesis that the PRI is sensitive to physiological dynamics while the others detect static factors related to canopy structure. Sensitivity analysis was performed on MCRM in order to better understand the effects of structure related parameters on the PRI simulations. Leaf area index (LAI) showed the most significant impact on MCRM-simulated PRI among the parameters studied. This research shows the importance of hyperspectral and narrow band sensor studies, and especially the necessity of including the green wavelengths (e.g., 531 nm) on satellites proposing to monitor carbon dynamics of terrestrial ecosystems.

Cheng, Yen-Ben↗
DOE OSTI2026

Contrasting Carbon–Water–Energy Dynamics in Perennial and Annual Bioenergy Agroecosystems Using Eddy Covariance and Interpretable Machine Learning

Understanding how agroecosystems respond to environmental variability is fundamental to predicting productivity and sustainability under a changing climate. We analyzed 55 site-years of high-frequency eddy covariance observations from five agroecosystems—two perennial grasses (miscanthus and switchgrass), two annual rotation systems (maize–soybean and sorghum–soybean), and a restored native prairie—to examine ecosystem-scale carbon, water, and energy fluxes. Using an interpretable machine-learning framework with regression tree ensembles, Shapley Additive Explanations, and Accumulated Local Effects, we quantified how environmental and temporal factors regulate gross primary productivity (GPP), evapotranspiration (ET), water-use efficiency, and the Bowen ratio. Perennials exhibited stronger physiological buffering and maintained fluxes across a broader range of temperature and moisture conditions, reflecting deeper rooting and persistent canopy cover. Annuals, in contrast, showed greater short-term variability and stronger coupling to atmospheric demand, with GPP and ET declining rapidly under low humidity or soil moisture. Differences in temperature sensitivity of Bowen ratio further revealed that perennials sustained proportionally greater sensible heat flux under cool conditions, whereas annuals exhibited constrained energy exchange when evaporative demand was low. Together, these results demonstrate that crop life cycle and canopy structure are fundamental determinants of ecosystem-scale carbon–water–energy coupling. By integrating long-term flux observations with interpretable machine learning, this study identifies the environmental drivers that shape agroecosystem function and highlights how conversion from annual to perennial feedstocks can enhance climatic resilience and alter land–atmosphere energy feedbacks. These findings provide a data-driven basis for improving crop and Earth-system models and for guiding bioenergy landscape design under future climate scenarios.

Accumulated Local Effects↗
DOE OSTI2023

Can upscaling ground nadir SIF to eddy covariance footprint improve the relationship between SIF and GPP in croplands?

Ground solar-induced chlorophyll fluorescence (SIF) is important for the mechanistic understanding of the dynamics of vegetation gross primary production (GPP) at fine spatiotemporal scales. However, eddy covariance (EC) observations generally cover larger footprint areas than ground SIF observations (a bare fiber with nadir), and this footprint mismatch between nadir SIF and GPP could complicate the canopy SIF-GPP relationships. Here, we upscaled nadir SIF observations to EC footprint and investigated the change in SIF-GPP relationships after the upscaling in cropland. We included 13 site-years data in our study, with seven site-years corn, four site-years soybeans, and two site-years miscanthus, all located in the US Corn Belt. All sites’ crop nadir SIF observations collected from the automated FluoSpec2 system (a hemispheric-nadir system) were upscaled to the GPP footprint-based SIF using vegetation indices (VIs) calculated from high spatiotemporal satellite reflectance data. We found that SIF-GPP relationships were not substantially changed after upscaling nadir SIF to GPP footprint at our crop sites planted with corn, soybean, and miscanthus, with R 2 change after the upscaling ranging from -0.007 to 0.051 and root mean square error (RMSE) difference from -0.658 to 0.095 umol m –2 s –1 relative to original nadir SIF-GPP relationships across all the site-years. The variation of the SIF-GPP relationship within each species across different site-years was similar between the original nadir SIF and upscaled SIF. Different VIs, EC footprint models, and satellite data led to marginal differences in the SIF-GPP relationships when upscaling nadir SIF to EC footprint. Furthermore, our study provided a methodological framework to correct this spatial mismatch between ground nadir SIF and GPP observations for croplands and potentially for other ecosystems. Our results also demonstrated that the spatial mismatch between ground nadir SIF and GPP might not significantly affect the SIF-GPP relationship in cropland that are largely homogeneous.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

BESS-STAIR: a framework to estimate daily, 30m, and all-weather crop evapotranspiration using multi-source satellite data for the US Corn Belt

Abstract. With increasing crop water demands and drought threats, mapping andmonitoring of cropland evapotranspiration (ET) at high spatial and temporalresolutions become increasingly critical for water management andsustainability. However, estimating ET from satellites for precise waterresource management is still challenging due to the limitations in bothexisting ET models and satellite input data. Specifically, the process of ETis complex and difficult to model, and existing satellite remote-sensing datacould not fulfill high resolutions in both space and time. To address theabove two issues, this study presents a new high spatiotemporal resolution ETmapping framework, i.e., BESS-STAIR, which integrates a satellite-drivenwater–carbon–energy coupled biophysical model, BESS (Breathing Earth SystemSimulator), with a generic and fully automated fusion algorithm, STAIR(SaTallite dAta IntegRation). In this framework, STAIR provides daily 30'mmultispectral surface reflectance by fusing Landsat and MODIS satellite datato derive a fine-resolution leaf area index and visible/near-infrared albedo,all of which, along with coarse-resolution meteorological and CO 2 data, are used to drive BESS to estimate gap-free 30 m resolution daily ET.We applied BESS-STAIR from 2000 through 2017 in six areas across the US CornBelt and validated BESS-STAIR ET estimations using flux-tower measurementsover 12 sites (85 site years). Results showed that BESS-STAIR daily ETachieved an overall R2=0.75, with root mean square error RMSE=0.93 mm d -1 and relative error RE =27.9 % when benchmarkedwith the flux measurements. In addition, BESS-STAIR ET estimations capturedthe spatial patterns, seasonal cycles, and interannual dynamics well indifferent sub-regions. The high performance of the BESS-STAIR frameworkprimarily resulted from (1) the implementation of coupled constraints onwater, carbon, and energy in BESS, (2) high-quality daily 30 m data from theSTAIR fusion algorithm, and (3) BESS's applicability under all-skyconditions. BESS-STAIR is calibration-free and has great potentials to be areliable tool for water resource management and precision agricultureapplications for the US Corn Belt and even worldwide given the globalcoverage of its input data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Influence of Vegetation Canopy Architecture on Dynamics of Spatiotemporal Light Distribution and its Effect on Land-Biosphere-Atmosphere Fluxes of Energy, Carbon, and Water

The dynamic spatiotemporal distribution of light and micro climatic variables (wind speed, atmospheric CO 2 concentration, humidity, pressure, and air temperature) within the plant canopy as determined by the canopy architecture play a critical role in land-biosphere-atmosphere (LBA) photosynthetic carbon uptake, transpiratory water loss and energy partitioning. Vegetation models that include effects of sun and shade flecks, leaf temperature and energy balance partitioning between sensible and latent heat fluxes, and reduced turbulent mixing in dense vegetation canopies can accurately predict canopy performance, particularly its shaded leaves, which can constitute 80% of leaf area and contribute up to 50% of carbon update and 65% of water loss. However, current vegetation models with their bulk characterization of shaded leaves poorly represent the complex three dimensional (3D) canopy architecture are insufficient to capture the spatiotemporal dynamics of shaded leaves. Here, we propose to employ an x-ray computed tomography (XCT) imaging technique in combination with an automated, high throughput, image processing pipeline (3DArch, to be developed) to obtain an explicit 3D triangulated surface mesh of a plant canopy (Fig 1). 3DArch would provide a much needed technological jump in 3D LBA modeling where computer generated artificial 3D models suffer from lack of high resolution detailed canopy architecture parameters. The 3D canopy surface mesh output from 3DArch is coupled with an explicit 3D forward ray tracing model (fastTracer) to obtain a high resolution representation of the dynamic spatiotemporal light environment within the canopy over the course of a day (Fig 1). The results from the light attenuation model are then coupled with a process based multi-scale bio-physical canopy and leaf model of photosynthesis (PlantGro) to get fluxes of carbon, water and energy at the leaf and canopy scale, in addition to estimating whole plant carbon uptake, and water loss. Our proposed model framework would be the first of its kind to provide a breakthrough capability in the field of process-based plant modeling that bridges the key technological gap in shaded leaf modeling through explicit representation of 3D canopy architecture. Computer generated artificial 3D model canopy simulations on grasses show that the canopy light distribution does not follow an exponential behavior as assumed in most 1D vegetation models, but rather exhibits a heterogeneous behavior, with a small but significant portion of interior canopy leaves experiencing peak light levels due to sun flecks while the rest of the canopy receives substantially lower light levels. These results indicate a bias in traditional models that overestimates shaded leaf contribution towards whole canopy photosynthesis by up to 15% and underestimate transpiration by up to 12% with an associated increase in sensible heat fluxes since photosynthesis, transpiration and energy partitioning are tightly coupled processes. However, these test results need to be validated using the 3D architecture obtained from a real plant imaged in 3D using 3DArch, rather than an artificial model plant. Our proposed model framework would allow us to identify inefficiencies in canopy light distribution and prescribe optimal canopy architectures (leaf optical properties, leaf area, leaf angles, row spacing, planting density, etc.) to traditional and genetic engineering based crop breeders, for maximizing target functions such as light use efficiency, water use efficiency, drought tolerance, crop yield, crop resilience etc., with important applications in biofuel crops such as sorghum, and switchgrass. Our model can also be applied for designing optimal canopy architecture that increase surface albedo as a mitigation strategy for global warming and projections of crop yield for future food and water security under climate change.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2003

Control of Interacting Vortex Flows at Subsonic and Transonic Speeds Using Passive Porosity

A wind tunnel experiment was conducted in the NASA Langley Research Center (LaRC) 8-foot Transonic Pressure Tunnel (TPT) to determine the effects of passive surface porosity on vortex flow interactions about a general research fighter configuration at subsonic and transonic speeds. Flow- through porosity was applied to a wind leading-edge extension (LEX) mounted to a 65 deg cropped delta wind model to promote large nose-down pitching moment increments at high angles of attack. Porosity decreased the vorticity shed from the LEX, which weakened the LEX vortex and altered the global interactions of the LEX and wing vortices at high angles of attack. Six-component forces and moments and wing upper surface static pressure distributions were obtained at free- stream Mach numbers of 0.50, 0.85, and 1.20, Reynolds number of 2.5(10(exp-6) per foot, angles of attack up to 30 deg and angles of sideslip to plus or minus 8 deg. The off-surface flow field was visualized in selected cross-planes using a laser vapor screen flow visualization technique. Test data were obtained with a centerline vertical tail and with alternate twin, wing-mounted vertical fins having 0 deg and 30 deg cant angles. In addition, the porosity of the LEX was compartmentalized to determine the sensitivity of the vortex- dominated aerodynamics to the location and level of porosity applied to the LEX.

NATO FURNISHED↗
DOE OSTI2021

Impact of vegetation physiology and phenology on watershed hydrology in a semiarid watershed in the Pacific Northwest in a changing climate

Concurrent with changes in climate conditions, increases in atmospheric composition and transport can affect land surface processes by regulating physiological and phenological responses of plants, and consequently perturb water, carbon, and nitrogen (N) dynamics at multiple scales. Howerver, the relationships bwtween plants physiological and phenological and land surface processes in a changing climate are less well known due to the limitation of representing palnts properties in traditional hydrological models. In this study, we use the Community Land Model version 5 (CLM5) to investigate how projected changes in large scale atmospheric conditions can affect hydrologic and biogeochemical dynamics in the Upper Columbia-Priest Rapids (UCPR) watershed, a typical semi-arid watershed located in the north-western United States dominated by croplands. Six numerical experiments considering climate change, CO2 concentration, and N deposition consistent with the Representative Concentration Pathways (RCPs) 4.5 and 8.5 are performed at 1-km resolution over the period of 2015-2099. Our results show that, compared with historical results (1985-2014), the elevated CO2 directly increases plants N uptake rates that provide additional leaf N to enhance photosynthesis by the end of 21st century (2070-2099). The higher GPP leads to changes in stomatal resistance and increased leaf area index (LAI) that affect evaporatranspiration (ET) and its components. As a result of stomatal responses to a higher CO2 concentration, ET over corn fields declines by 24.6 mm yr-1 under RCP4.5, and by 64.0 mm yr-1 under RCP8.5, respectively by the end of the century from its historical mean of 428 mm yr-1. Meanwhile, ET over soybean fields increases by 27.7 mm yr-1 under RCP4.5 and 9.98 mm yr-1 under RCP8.5 from its historical mean of 507 mm yr-1 as a result of CO2 fertilization as soybeans are able to uptake more N to increase LAI significantly. Furtherfore, the changes in physiological and phenological in corn and soybean can strongly modulate water budget terms over the watershed in response to expected changes in greenhouse gas concentrations and atmospheric composition. Additionally, increases in CO2 concentration and N deposition are beneficial for improving water use efficiency of corn and soybean, therefore lead to less water consumption per unit gram of carbon produced from these crops. Our study elucidates the need to jointly consider effects of surface climate and atmospheric composition and transport in understanding and predicting watershed hydrologic and biogeochemical responses, especially for an agricultural watershed.

Zhu, Bowen↗