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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 793 records · Page 44

Point Cloud Based Mapping of Understory Shrub Fuel Distribution, Estimation of Fuel Consumption and Relationship to Pyrolysis Gas Emissions on Experimental Prescribed Burns

Forest fires spread via production and combustion of pyrolysis gases in the understory. The goal of the present paper is to understand the spatial location, distribution, and fraction (relative to the overstory) of understory plants, in this case, sparkleberry shrub, namely its degree of understory consumption upon burn, and to search for correlations between the degree of shrub consumption to the composition of emitted pyrolysis gases. Data were collected in situ at seven small experimental prescribed burns at Ft. Jackson, an army base in South Carolina, USA. Using airborne laser scanning (ALS) to map overstory tree crowns and terrestrial laser scanning (TLS) to characterize understory shrub fuel density, both pre- and postburn estimates of sparkleberry coverage were obtained. Sparkleberry clump polygons were manually digitized from a UAV-derived orthoimage of the understory and intersected with the TLS point cloud-derived rasters of pre- and postburn shrub fuel bulk density; these were compared in relation to overstory crown cover as well as to ground truth. Shrub fuel consumption was estimated from the digitized images; sparkleberry clump distributions were generally found to not correlate well to the overstory tree crowns, suggesting it is shade-tolerant. Moreover, no relationship was found between the magnitude of the fuel consumption and the chemical composition of pyrolysis gases, even though mixing ratios of 25 individual gases were measured.

54 ENVIRONMENTAL SCIENCES↗

Detecting Coastal Wetland Degradation by Combining Remote Sensing and Hydrologic Modeling

Sea-level rise and climate change stresses pose increasing threats to coastal wetlands that are vital to wildlife habitats, carbon sequestration, water supply, and other ecosystem services with global significance. However, existing studies are limited in individual sites, and large-scale mapping of coastal wetland degradation patterns over a long period is rare. Our study developed a new framework to detect spatial and temporal patterns of coastal wetland degradation by analyzing fine-scale, long-term remotely sensed Normalized Difference Vegetation Index (NDVI) data. Then, this framework was tested to track the degradation of coastal wetlands at the Alligator River National Wildlife Refuge (ARNWR) in North Carolina, United States, during the period from 1995 to 2019. We identified six types of coastal wetland degradation in the study area. Most of the detected degradation was located within 2 km from the shoreline and occurred in the past five years. Further, we used a state-of-the-art coastal hydrologic model, PIHM-Wetland, to investigate key hydrologic processes/variables that control the coastal wetland degradation. The temporal and spatial distributions of simulated coastal flooding and saltwater intrusion confirmed the location and timing of wetland degradation detected by remote sensing. The combined method also quantified the possible critical thresholds of water tables for wetland degradation. The remote sensing–hydrologic model integrated scheme proposed in this study provides a new tool for detecting and understanding coastal wetland degradation mechanisms. Our study approach can also be extended to other coastal wetland regions to understand how climate change and sea-level rise impact wetland transformations.

54 ENVIRONMENTAL SCIENCES↗

Preclinical Assessment of Volumetric Modulated Arc Therapy for Total Marrow Irradiation

Purpose: A preclinical investigation was undertaken to explore a treatment technique for total marrow irradiation using RapidArc, a volumetric modulated arc technique. Materials and Methods: Computed tomography datasets of 5 patients were included. Plans with eight overlapping coaxial arcs were optimized for 6-MV photon beams. Dose prescription was 12 Gy in 2 Gy per fraction, normalized so that 100% isodose covered 85% of the planning target volume (PTV). The PTV consisted of the whole skeleton (including ribs and sternum), from the top of the skull to the medium distal third of the femurs. Planning objectives for organs at risk (OARs) were constrained to a median dose <6 to 7 Gy. OARs included brain, eyes, oral cavity, parotids, thyroid, lungs, heart, kidneys, liver, spleen, stomach, abdominal cavity, bladder, rectum, and genitals. Pretreatment quality assurance consisted of portal dosimetry comparisons, scoring the delivery to calculation agreement with the gamma agreement index. Results: The median total body volume in the study was 57 liters (range, 49-81 liters), for an average diameter of 47 cm (range, 46-53 cm) and a total length ranging from 95 to 112 cm. The median PTV volume was 6.8 liters (range, 5.8-10.8 liters). The mean dose to PTV was 109% (range, 107-112%). The global mean of median dose to all OARs was 4.9 Gy (range, 4.5-5.1 Gy over the 5 patients). The individual mean of median doses per organ ranged from 2.3 Gy (oral cavity) to 7.3 Gy (bowels cavity). Preclinical quality assurance resulted in a mean gamma agreement index of 94.3 {+-} 5.1%. The delivery time measured from quality assurance runs was 13 minutes. Conclusion: Sparing of normal tissues with adequate coverage of skeletal bones was shown to be feasible with RapidArc. Pretreatment quality assurance measurements confirmed the technical agreement between expected and actually delivered dose distributions, suggesting the possibility of incorporating this technique in the treatment options for patients.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Possibilities of using practical limitations of an eavesdropper in quantum cryptography

An important advantage of quantum cryptography over classical cryptography consists in the fact that the security of the transmitted keys is not related to the assumptions about the eavesdropper capabilities and is guaranteed by the laws of nature. Nevertheless, in some situations it makes sense to consider some reasonable assumptions about the eavesdropper capabilities, which can increase the secret key distribution rate. Methods are proposed for legitimate users to employ some practical limitations, and attacks are constructed that the eavesdropper can apply under the conditions of these limitations. (paper)

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Predicting Switchgrass Biomass Yields Using a Spectral Vegetation Index Derived from Multispectral Satellite Imagery

Successful scaling of perennial bioenergy crop production requires a landscape design that optimizes the benefits of finite lands for people, communities, and environments. Utilizing marginal areas is the key to sustainable bioenergy crop production (Ssegane et al., 2015, 2016). Marginal areas are often small-sized lands and unevenly distributed across the agricultural landscape (Ssegane et al., 2016); thus, a systematic, semi-automated remote sensing method is needed as an effective means of estimating bioenergy crop yields across landscapes. Argonne National Laboratory (Argonne) is currently developing a tool, Scaling Up Perennial Bioenergy Economics and Ecosystem Services Tool (SUPERBEEST), to identify marginal agricultural lands and quantify environmental and economic effects of perennial bioenergy crop production systems. The tool aims to provide users a path to foster the sustainable and productive integration of bioenergy crops in the Midwestern agricultural landscape. Reliable, cost-effective, and timely estimation of bioenergy crop yields using remote sensing would help calculate and track the success of integrated bioenergy crops in the landscape for those communities. Argonne previously conducted feasibility studies for estimating biomass yields for bioenergy feedstock, corn and perennial grass using spectral vegetation indices (SVIs)1 derived from optical imagery (Hamada et al., 2015, 2021). In both studies, SVIs, more specifically those sensitive to plant chlorophyll or nitrogen contents, showed potential for estimating or predicting biomass yields with a correlation of determination (R2) ranging from 0.54 to 0.96, indicating a value for further investigation as a viable means of quantifying bioenergy feedstock production across large landscapes. Thus, the goal of this study is to evaluate the feasibility of use of SVIs as a means of estimating or predicting switchgrass biomass yields at harvest using publicly available multispectral satellite imagery. The feasibility analysis was performed using four study areas of mature switchgrass located in Virginia. Objectives are to (1) examine the SVIs and establish their relationships with switchgrass biomass yields at harvest, (2) develop a parsimonious image processing model for predicting at-harvest yields by applying the relationships with the most promising spectral index and (3) map switchgrass yields predicted by the image processing model across the study sites. The calibration to field data will rely on switchgrass biomass yields determined by the baling method, representing a potential challenge to the analysis but an important practical aspect for future applications. With this research design, the study aimed to gain insights into enabling remote sensing-based estimation of bioenergy crop yields in a reliable, cost- effective, and timely manner across large, heterogeneous landscapes.

09 BIOMASS FUELS↗

Verification of an Algorithm for Processing Data from Multifrequency Lidar Sensing of Aerosols

We consider an algorithm for processing lidar signals, which for a lidar path with constant qualitative aerosol characteristics (particle size distribution in each aerosol mode, complex refractive index of the particles) lets us obtain a concentration profile for the fine aerosol component without using additional a priori information. The algorithm was verified based on lidar data from the Institute of Physics (National Academy of Sciences of Belarus). The vertical volume concentration profile for fine particles obtained by our algorithm is quite consistent with the results of its independent retrieval, based on integrated processing of lidar and sun photometer data.

42 ENGINEERING↗

Chemical Sensing Strategies for Real-Time Monitoring of Transformer Oil: A Review

Power transformers are a central component in the field of energy distribution and transmission. The early recognition of incipient faults in operating transformers is substantially cost effective by lessening impromptu blackouts. A standout amongst the most responsive and dependable strategies utilized for assessing the health of oil filled electrical equipment is dissolved gas analysis (DGA). Nowadays, there is an expanding requirement for better nonintrusive diagnostic and online monitoring tools to survey the internal state of the transformers. Chemical sensors are viewed as a key innovation for condition monitoring of transformer health, coordinating the non-invasiveness with typical sensor features, such as cost, usability, portability, and the integration with the data networks. Low-cost chemical sensors-based DGA techniques are expected to drastically augment the diagnostic abilities empowering the deployment on a broader range of oil filled power assets. The recent development involves both specific sensors designed to detect individual dissolved gas in transformer oil and non-specific sensors, operated in near ambient conditions, with the potential to be applied in a DGA system. In this paper, general background and operating guidelines of DGA are presented to address the origin of the gas formation, methods for their detection and the interpretation of the results by data analytics. The recent significant interest and advancements in chemical sensors to DGA applications are reviewed. Lastly, future research perspectives and challenges for the development of novel DGA chemical sensors are also discussed.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Synthesis and Characterization of Pd-based Nanomaterials

Bimetallic nanoparticles (BNPs) consist of two different types of metals or alloys that are bonded together. Unique properties such as optical, electronic, thermal, and catalytic effects differ for each type of BNP. Important BNPs range from Au-Pd, Ag- Pt, Au-Pt, and Ag-Ni. Pd bimetallic nanoparticles are of interest due to their many applications such as catalysis and sensing. Bimetallic catalysts have increase reaction rates and have improved catalyst stability through the geometry and ligand distribution. Pd nanoparticles are considered to be a strong catalyst due to their high activity at low temperatures and high tolerance to moisture. The catalytic properties of bimetallic nanoparticles depend on the structural properties such as size and shape. Core-shell, hollow structure, and multi-shell alloy are three possible structures nanoparticles can form as bimetallic catalysts. BNPs can be synthesized through different methods to control the size, shape, and structure. To obtain different morphologies, a variety of methods can be performed. Different methods can range from the usage of the glancing angle deposition (GLAD) to the galvanic replacement reaction, but the methods all depend on the properties of the metals. The galvanic displacement reaction was the method used to obtain Pd-based nanoparticles. This reaction is best know for obtaining hollow shaped NPs. To determine what redox process was preformed, the activity series of metals was used. From the activity series of metals, silver (Ag) was selected to preform Pd-based nanoparticles. Objectives: Synthesize Ag nanoparticles and Ag-Pd nanoparticles to understand the morphology. Characterize the synthesized nanoparticles using scanning electron microscopy (SEM), phase analysis light scattering (PALS), dynamic light scattering (DLS), energy dispersive X-ray spectroscopy (EDS), and UV-Vis spectroscopy. Results: In the UV-Vis spectrum, the Ag-Pd bimetallic NP's plasmon band decreased as the volume of palladium increased. The surface charge increases as the concentration of palladium increases. The Pd{sup 2+} ions interact with the sodium citrate surface, and decrease the negative charge. Conclusion: Ag-Pd nanoparticles were successfully created and stabilized with sodium citrate. The addition of Pd decreased the prominent plasmon band of the Ag nanoparticles. The SEM analysis showed that Ag nanoparticles had a well-defined structure, while the Ag-Pd nanoparticles showed hollow and rough structure. The EDX analysis confirmed the presence of silver and palladium. This material can be used in many industrial and research fields such as organic synthesis, fuel cells, and environmental sensing and remediation.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Aerosol optical depth increase in partly cloudy conditions

Remote sensing observations of aerosol from surface and satellite instruments are extensively used for atmospheric and climate research. From passive sensors, the apparent cloud-free atmosphere in the vicinity of clouds often appears to be brighter then further away from the clouds, leading to an enhancement in the retrieved aerosol optical depth. Mechanisms contributing to this enhancement, including contamination by undetected clouds, hygroscopic growth of aerosol particles, and meteorological conditions, have been debated in recent literature, but an extent to which each of these factors influence the observed enhancement is poorly known. Here we used 11 years of daily global observations at 10x10 km2 resolution from the MODIS on the NASA Terra satellite to quantify as a function of cloud fraction (CF). Our analysis reveals that, averaged over the globe, the clear sky is enhanced by ? = 0.05 which corresponds to relative enhancements of 25% in cloudy conditions (CF=0.8-0.9) compared with relatively clear conditions (CF=0.1-0.2). Unlike the absolute enhancement ?, the relative increase in ? is rather consistent in all seasons and is 25-35% in the subtropics and 15-25% at mid and higher latitudes. Using a simple Gaussian probability density function model to connect cloud cover and the distribution of relative humidity, we argue that much of the enhancement is consistent with aerosol hygroscopic growth in the humid environment surrounding clouds. Consideration of these cloud-dependent effects will facilitate understanding aerosol-cloud interactions and reduce the uncertainty in estimates of aerosol radiative forcing by global climate models.

54 ENVIRONMENTAL SCIENCES↗

Transient Cosmic-ray Events beyond the Heliopause: Interpreting Voyager-1 Observations

In 2013 March and 2014 May, Voyager-1 ( V1 ) experienced small but significant increases in the flux of galactic cosmic rays (GCRs) in the hundred MeV/n range. Additionally, V1 also saw episodic depletion of GCR flux around perpendicular pitch angles. We discuss the pitch-angle distribution and the time profiles of these events. In a previous paper, we interpreted the 2013 “bump” as the GCRs remotely sensing a shock that reached the magnetic field line passing through V1 : particles gained energy as they were reflected on the approaching region of the stronger magnetic field of the disturbance. Here, we point out that energy gain is not restricted to reflected particles—GCRs passing through the disturbance also gain energy. The effect should be present in a broad range of pitch angles with the maximum increase of GCR intensity predicted to occur at the critical reflection angle. In this paper, the shock is not step-like, but a gradual increase of the magnetic field strength, B , taking a few days, in agreement with V1 measurements. This smoothens the profile of the predicted bump in the GCR flux. We also address the linear episodic decreases seen around perpendicular pitch angles. These events are interpreted in terms of adiabatic cooling behind the shock due to the slow weakening of B . We present simple numerical model calculations and find that a gradual shock followed by a slow decrease of B , as observed, may account for both the episodic increases and the anisotropic depletion of GCR fluxes.

79 ASTRONOMY AND ASTROPHYSICS↗

Nanosensors as Reservoir Engineering Tools to Map Insitu Temperature Distributions in Geothermal Reservoirs

The feasibility of using nanosensors to measure temperature distribution and predict thermal breakthrough in geothermal reservoirs is addressed in this report. Four candidate sensors were identified: melting tin-bismuth alloy nanoparticles, silica nanoparticles with covalently-attached dye, hollow silica nanoparticles with encapsulated dye and impermeable melting shells, and dye-polymer composite time-temperature indicators. Four main challenges associated with the successful implementation of temperature nanosensors were identified: nanoparticle mobility in porous and fractured media, the collection and detection of nanoparticles at the production well, engineering temperature sensing mechanisms that are both detectable and irreversible, and inferring the spatial geolocation of temperature measurements in order to map temperature distribution. Initial experiments were carried out to investigate each of these challenges. It was demonstrated in a slim-tube injection experiment that it is possible to transport silica nanoparticles over large distances through porous media. The feasibility of magnetic collection of nanoparticles from produced fluid was evaluated experimentally, and it was estimated that 3% of the injected nanoparticles were recovered in a prototype magnetic collection device. An analysis technique was tailored to nanosensors with a dye-release mechanism to estimate temperature measurement geolocation by analyzing the return curve of the released dye. This technique was used in a hypothetical example problem, and good estimates of geolocation were achieved. Tin-bismuth alloy nanoparticles were synthesized using a sonochemical method, and a bench heating experiment was performed using these nanoparticles. Particle growth due to melting was observed, indicating that tin-bismuth nanoparticles have potential as temperature nanosensors

15 GEOTHERMAL ENERGY↗

Securing Solar for the Grid: Extreme Control Whitepaper

Emerging standards outlining desired behaviors for Distributed Energy Resources (DER), such as IEEE 1547-2018, define several device-level control functions to regulate DER power injections/consumptions in response to locally sensed grid conditions. The ability to adjust settings of aggregations of DER with standardized embedded control functionality constitutes a mechanism which can, potentially, create undesirable or deleterious effects on the power grid. The purpose of this white paper is to highlight unintended effects stemming from improperly tuned embedded control functions in Distributed Energy Resources (DER), which could be exploited by a malicious entity as a means to attack the power grid.

14 SOLAR ENERGY↗

Enhanced spontaneous DNA twisting/bending fluctuations unveiled by fluorescence lifetime distributions promote mismatch recognition by the Rad4 nucleotide excision repair complex

Rad4/XPC recognizes diverse DNA lesions including ultraviolet-photolesions and carcinogen-DNA adducts, initiating nucleotide excision repair. Studies have suggested that Rad4/XPC senses lesion-induced helix-destabilization to flip out nucleotides from damaged DNA sites. However, characterizing how DNA deformability and/or distortions impact recognition has been challenging. Here, using fluorescence lifetime measurements empowered by a maximum entropy algorithm, we mapped the conformational heterogeneities of artificially destabilized mismatched DNA substrates of varying Rad4-binding specificities. The conformational distributions, as probed by FRET between a cytosine-analog pair exquisitely sensitive to DNA twisting/bending, reveal a direct connection between intrinsic DNA deformability and Rad4 recognition. High-specificity CCC/CCC mismatch, free in solution, sampled a strikingly broad range of conformations from B-DNA-like to highly distorted conformations that resembled those observed with Rad4 bound; the extent of these distortions increased with bound Rad4 and with temperature. Conversely, the non-specific TAT/TAT mismatch had a homogeneous, B-DNA-like conformation. Molecular dynamics simulations also revealed a wide distribution of conformations for CCC/CCC, complementing experimental findings. We propose that intrinsic deformability promotes Rad4 damage recognition, perhaps by stalling a diffusing protein and/or facilitating ‘conformational capture’ of pre-distorted damaged sites. Surprisingly, even mismatched DNA specifically bound to Rad4 remains highly dynamic, a feature that may reflect the versatility of Rad4/XPC to recognize many structurally dissimilar lesions

59 BASIC BIOLOGICAL SCIENCES↗

Extended Forward Sensitivity Analysis for Uncertainty Quantification

Verification and validation (V&V) are playing more important roles to quantify uncertainties and realize high fidelity simulations in engineering system analyses, such as transients happened in a complex nuclear reactor system. Traditional V&V in the reactor system analysis focused more on the validation part or did not differentiate verification and validation. The traditional approach to uncertainty quantification is based on a 'black box' approach. The simulation tool is treated as an unknown signal generator, a distribution of inputs according to assumed probability density functions is sent in and the distribution of the outputs is measured and correlated back to the original input distribution. The 'black box' method mixes numerical errors with all other uncertainties. It is also not efficient to perform sensitivity analysis. Contrary to the 'black box' method, a more efficient sensitivity approach can take advantage of intimate knowledge of the simulation code. In these types of approaches equations for the propagation of uncertainty are constructed and the sensitivities are directly solved for as variables in the simulation. This paper presents the forward sensitivity analysis as a method to help uncertainty qualification. By including time step and potentially spatial step as special sensitivity parameters, the forward sensitivity method is extended as one method to quantify numerical errors. Note that by integrating local truncation errors over the whole system through the forward sensitivity analysis process, the generated time step and spatial step sensitivity information reflect global numerical errors. The discretization errors can be systematically compared against uncertainties due to other physical parameters. This extension makes the forward sensitivity method a much more powerful tool to help uncertainty qualification. By knowing the relative sensitivity of time and space steps with other interested physical parameters, the simulation is allowed to run at optimized time and space steps without affecting the confidence of the physical parameter sensitivity results. The time and space steps forward sensitivity analysis method can also replace the traditional time step and grid convergence study with much less computational cost. Several well defined benchmark problems with manufactured solutions are utilized to demonstrate the extended forward sensitivity analysis method. All the physical solutions, parameter sensitivity solutions, even the time step sensitivity in one case, have analytical forms, which allows the verification to be performed in the strictest sense.

97 MATHEMATICS AND COMPUTING↗

Enabling a Decentralized Smart Grid Using Autonomous Edge Control Devices

As a large number of distributed devices are connected to the modern smart grid, the traditional centralized connectivity models fail to provide economic value. These models have relied on sending data to the cloud for processing and receiving commands to exert control actions, resulting in an ‘on-demand system’ with high bandwidth, low latency and an overload of data on the cloud. For realizing a decentralized system, there is a strong need to embed intelligence at the ‘edge of the network’. These intelligent devices, capable of sensing, local data processing and exerting control actions, report only actionable information to the cloud, acting as an edge control node. The system can then function autonomously, without constant cloud inputs, tolerating longer delays in communication, making the overall system ultra-low cost. The Global Asset Monitoring, Management and Analytics (GAMMA) Platform is a novel ultralow- cost, secure platform that operates through a Bluetooth based delay tolerant network. It relies on so-called ‘data mules’ to bridge the last mile connectivity gap in an inherently secure way. Due to this model, the platform requires no in-country certifications, does not rely on a dedicated backhaul technology and is immune to technology migration. This architecture also addresses some gaps identified in traditional IoT-based solutions in remote areas and sparse connectivity. A functional unit of the edge computing node has been built, taking into account various constraints like costs, customizations, data storage, cybersecurity and power management. Here, the platform has been built, deployed and has demonstrated distributed smart grid applications like power quality sensing, automated metering infrastructure and utility asset monitoring.

42 ENGINEERING↗

Fano resonances based on plasmonic square resonator with high figure of merits and its application in glucose concentrations sensing

A compact plasmonic structure is proposed employing symmetrical metal–insulator-metal (MIM) waveguides coupled to the square resonator for nano-sensing applications, especially for water glucose sensing. Finite difference time domain method is chosen to derivate the output characteristics and magnetic-field distributions. Outcomes illustrate the Fano resonance in the output characteristics can be simply managed by varying the inner and outer lengths and the refractive index of the square cavity. Also, between Fano resonance wavelength and resonator length, a linear behavior exists. These features suggest that physical parameters provide flexibility to propose the structure. Our plasmonic device produces a sensitivity and figure of merit of about 6400 nm/RIU and 1 × 10{sup 4}, respectively. By utilizing the mathematical model for water refractive index, we aim to develop a sensor structure to detect the glucose concentration in water. This designed sensor may discover significant applications in future nanosensing domain.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Uncertainty Quantification of Extratropical Forest Biomass in CMIP5 Models over the Northern Hemisphere

Abstract Simplified representations of processes influencing forest biomass in Earth system models (ESMs) contribute to large uncertainty in projections. We evaluate forest biomass from eight ESMs outputs archived in the Coupled Model Intercomparison Project Phase 5 (CMIP5) using the biomass data synthesized from radar remote sensing and ground-based observations across northern extratropical latitudes. ESMs exhibit large biases in the forest distribution, forest fraction, and mass of carbon pools that contribute to uncertainty in forest total biomass (biases range from −20 Pg C to 135 Pg C). Forest total biomass is primarily positively correlated with precipitation variations, with surface temperature becoming equally important at higher latitudes, in both simulations and observations. Relatively small differences in forest biomass between the pre-industrial period and the contemporary period indicate uncertainties in forest biomass were introduced in the pre-industrial model equilibration (spin-up), suggesting parametric or structural model differences are a larger source of uncertainty than differences in transient responses. Our findings emphasize the importance of improved (1) models of carbon allocation to biomass compartments, (2) distribution of vegetation types in models, and (3) reproduction of pre-industrial vegetation conditions, in order to reduce the uncertainty in forest biomass simulated by ESMs.

54 ENVIRONMENTAL SCIENCES↗