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The main purpose of this grant was to gain a detailed understanding of the roles of soil microbes in post-fire ecosystems and their interaction with soil carbon storage. Ultimately, this knowledge could be used to facilitate post-fire recovery and carbon storage in the increasingly large areas of post-fire ecosystems.
This data package is associated with the publication “Ecosystem leaf area, gross primary production, and evapotranspiration responses to wildfire in the Columbia River Basin” submitted to Biogeosciences (Shi et al., 2024; doi: 10.22541/au.171053013.30286044/v1). In this research, data products, leaf area index (LAI), gross primary production (GPP), and evapotranspiration (ET), from the Moderate Resolution Imaging Spectroradiometer (MODIS) are used to quantify the resistance and resilience of different ecosystem types in the Columbia River Basin (CRB). A machine learning algorithm, random forest (RF), was used to examine the impacts of precipitation, vapor pressure deficit (VPD), and burn severity from Monitoring Trends in Burn Severity (MTBS) on ecosystem resilience. The data package includes the processed MODIS data products, precipitation, VPD, and burn severity in 138 fire regions in CRB and the input files for RF model training. This data package includes six folders. The MODIS products are included in three MODIS_* folders with shell scripts for data clipping and *ncl files for data processing: (1) “/MODIS_LAI_CRB”; (2) “/MODIS_GPP_CRB”; and (3) “/MODIS_ET_CRB”. All the processed data for each fire event are NetCDF formatted. The MTBS burn severity data and the shell and *ncl scripts used for data processing are in the folder named (4) “MTBS_fire”. The ERA meteorological fields and the data processing scritps are in (5) “ERA_Var_CR”. All the scripts for figure development are in the format of *ncl and in the folder (6) “paper_scripts”. See the file ending in “flmd.csv” for a list of all files contained in this data package and descriptions for each. Tabular column headers and units are described in the data dictionary file ending in “dd.csv”.
The U.S. Department of Defense (DOD), Department of Energy (DOE), and other organizations maintain operational nuclear explosion and atmospheric dispersion models to provide critical guidance on the expected effects of an accidental or deliberate explosion of a nuclear weapon (in this paper simply referred to as “device”). To be effective, these models must represent, as accurately as possible, the complex interactions of the blast, fire, and residual radiological hazards with the environment and population. One hundred atmospheric nuclear tests that form the basis for many models were conducted at the Nevada Test Site (NTS) (now referred to as the Nevada Nuclear Security Site, NNSS) in a dry desert environment. Other environments should be studied, but have less data available and are beyond the scope of the work presented in this paper. The debris clouds produced by the NTS tests, frequently called “mushroom clouds,” are familiar, with common structural elements such as a buoyant cap connected to a skirt of raised dust at the desert surface by a thin, dirt-filled stem. The film scanning project at LLNL has investigated historical film records of nuclear weapons tests. Here, we summarize findings showing that the mushroom cloud behavior for historic U.S. tests conducted in Nevada, has similar characteristics based on the distance of the device from the ground surface or Height of Burst (HOB), scaled by the energy release, or yield, of the device. This scaled height is referred to as the scaled-height-of-burst (SHOB). The findings discussed below show that mushroom clouds look and behave similarly when detonated at the same SHOB. The amount of residual radiation that is produced by a nuclear detonation is proportional to the yield. But, the amount of that residual radiation that actually becomes local fallout is strongly dependent on the SHOB and the type of surface over which the detonation occurs. In order to develop a more comprehensive model that predicts the fraction of the residual radiation that becomes local fallout, it is convenient to define a series of regimes based on SHOB values in which all detonations that occur within a given regime can be modeled using the same algorithms. The purpose of this paper is to provide a framework for defining different regimes, and, in a qualitative way, a basic understanding of the fundamental characteristics of each of these regimes.
Wildfire leads to the deposition of an ash layer at the land surface. This material typically has alkaline properties, abundant pyrogenic carbon, and elevated metal concentrations compared to surface soils. Here, we compare ash and surface soils collected three weeks and two years after the Glass Fire (St. Helena, California, USA) to determine how wildfire affects the speciation and reactivity of iron (Fe) and manganese (Mn), two micronutrients that influence many soil processes. Synchrotron-based analyses revealed that wildfire generated stable Fe oxides like maghemite (γ-Fe 2 O 3 ) and hematite (α-Fe 2 O 3 ) that likely derived from surface soil or mineral dust rather than the vegetation, and Mn oxides such as hausmannite (Mn 3 O 4 ) and bixbyite (Mn 2 O 3 ) that derived primarily from aboveground biomass. However, these pyrogenic minerals were absent in soils collected two years after the fire. Their loss from the soil surface may result from a combination of erosion, translocation to deeper soil layers, and chemical weathering. The latter hypothesis is supported by the enhanced mobilization of Fe and Mn from ash upon the addition of pyoverdine, a model biogenic ligand. The increased mobility of micronutrients from fire-derived minerals in ash in response to microbial and root exudation may facilitate postfire soil and vegetation recovery.
Background: Wildfires are becoming more severe, so we need improved tools to predict them over a wide range of conditions and scales. One approach towards this goal entails the use of coupled fire/atmosphere modelling tools. Although significant progress has been made in advancing their physical fidelity, existing tools have not taken full advantage of emerging programming paradigms and computing architectures to enable high-resolution wildfire simulations. Aims: The aim of this study was to present a new framework that enables landscape-scale wildfire simulations with physical representation of combustion at an affordable cost. Methods: We developed a coupled fire/atmosphere simulation framework using TensorFlow, which enables efficient and scalable computations on Tensor Processing Units. Key Results: Simulation results for a prescribed fire were compared with experimental data. Predicted fire behavior and statistical analysis for fire spread rate, scar area, and intermittency showed overall reasonable agreement. Scalability analysis was performed, showing close to linear scaling. Conclusions: While mesh refinement was shown to have less impact on global quantities, such as fire scar area and spread rate, it benefits predictions of intermittent fire behavior, buoyancy-driven dynamics, and small-scale turbulent motion. Implications: This new simulation framework is efficient in capturing both global quantities and unsteady dynamics of wildfires at high spatial resolutions.
A short-duration but high-impact air quality event occurred on 28 November 2018 along the Rio Grande Valley of New Mexico. This fire occurred outside the typical wildfire season, and greatly impacted the air quality in Socorro, NM, and the surroundings. Measurements were taken during the event using an aerosol light scattering technique (integrating nephelometer) and a particulate mass concentration monitor (DustTrak PM optical monitor). The instruments sampled the ambient air during the event on the campus of the New Mexico Institute of Mining and Technology in Socorro, New Mexico. The peak values on a 5-min basis of light scattering and the PM mass concentration reached 470 Mm -1 and 270 µg/m 3 , respectively. We examined the meteorological context of the event using local meteorological data and back trajectories using the NOAA HYSPLIT model to determine atmospheric transport and possible sources. Several fires, both prescribed and wildfires, occurred in the region including a prescribed burn at Bosque del Apache National Wildlife Refuge (17 km south-southeast of the receptor site). The data suggest that the prescribed burn at Bosque del Apache was the dominant contributor due to transport evidence and the event’s narrow spatiotemporal extent. The increasing importance of restoring ecosystem function using prescribed fire in wildland fire management will likely lead to more frequent air quality impacts and sets up policy tradeoffs that require a balance between these public goals. This study examines the evidence of the effects of a prescribed fire in a protected wildland area impacting the air quality in a nearby populated area.
Forestation can contribute to climate change mitigation. However, increasing frequency and intensity of climate extremes are posed to have profound impact on forests and consequently on the mitigation potential of forestation efforts. In this perspective, we critically assess forestation-reliant climate mitigation scenarios from five different integrated assessment models (IAMs) by showcasing the spatially explicit exposure of forests to fire weather and the simulated increase in global annual burned area. We provide a detailed description of the feedback from climate change to forest carbon uptake in IAMs. Few IAMs are currently accounting for feedback mechanisms like loss from fire disturbance. Consequently, many forestation areas proposed by IAM scenarios will be exposed to fire-promoting weather conditions and without costly prevention measures might be object to frequent burning. We conclude that the actual climate mitigation portfolio in IAM scenarios is subject to substantial uncertainty and that the risk of overly optimistic estimates of negative emission potential of forestation should be avoided. As a way forward we propose how to integrate more detailed climate information when modeling climate mitigation pathways heavily relying on forestation.
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Changes to soil hydraulic properties that reduce infiltration capacity following fire can increase flash flood risks. These risks are exacerbated by rainfall intensification associated with a warming climate. However, the potential effects of climate-change-driven rainfall intensification on postfire floods remain largely unexplored. Using rainfall and runoff observations from a 49.4 km 2 watershed in southern Arizona, USA, and a hydrologic model (KINEROS2), we examined the temporal evolution following a historic fire of three crucial hydrologic parameters: soil saturated hydraulic conductivity (K sp ), net capillary drive (G p ), and hydraulic roughness (n c ). We explored how the effect of fire on these parameters may influence peak flow under future climate scenarios derived from CMIP6, specifically the medium emissions scenario (SSP2-4.5) and high emissions scenario (SSP5-8.5). Results demonstrate an increase in K sp from 11 mm h −1 in the first postfire year to 60 mm h −1 in postfire year 3. G p similarly increased from 19 mm in the first postfire year to 30 mm in the third, while nc was relatively constant. The highest simulated Q p occurred in the first postfire year. Under the SSP2-4.5 scenario, the likelihood of a 100-year flood is projected to be twice as large by the middle of the century relative to its historical magnitude. Simulations further indicate that the maximum expected discharge associated with a postfire flood, as derived from historical data, could be triggered by a 10-year rainstorm under the SSP5-8.5 scenario by the late century. Simulations also demonstrate that rainfall intensification will lead to greater persistence of elevated flood hazards following fire by the late century under both the SSP2-4.5 and the SSP5-8.5 scenarios.
Dynamic networks composed of constituents that break and reform bonds reversibly are ubiquitous in nature owing to their modular architectures that enable functions like energy dissipation, self-healing, and even activity. While bond breaking depends only on the current configuration of attachment in these networks, reattachment depends also on the proximity of constituents. Therefore, dynamic networks composed of macroscale constituents (not benefited by the secondary interactions cohering analogous networks composed of molecular-scale constituents) must rely on primary bonds for cohesion and self-repair. Toward understanding how such macroscale networks might adaptively achieve this, we explore the uniaxial tensile response of 2D rafts composed of interlinked fire ants ( S. invicta ). Through experiments and discrete numerical modeling, we find that ant rafts adaptively stabilize their bonded ant-to-ant interactions in response to tensile strains, indicating catch bond dynamics. Consequently, low-strain rates that should theoretically induce creep mechanics of these rafts instead induce elastic-like response. Our results suggest that this force-stabilization delays dissolution of the rafts and improves toughness. Nevertheless, above 35 % strain low cohesion and stress localization cause nucleation and growth of voids whose coalescence patterns result from force-stabilization. These voids mitigate structural repair until initial raft densities are restored and ants can reconnect across defects. However mechanical recovery of ant rafts during cyclic loading suggests that—even upon reinstatement of initial densities—ants exhibit slower repair kinetics if they were recently loaded at faster strain rates. These results exemplify fire ants’ status as active agents capable of memory-driven, stimuli-response for potential inspiration of adaptive structural materials.
Wildfires are important ecosystem processes that have a significant impact on terrestrial vegetation, environment, and climate. This study investigates how future wildfire risk and activities could change under 1.5 °C and 2.0 °C warming scenarios relative to pre-industrial levels using a modified McArthur Forest Fire Danger Index (FFDIn) and the CLM4.5-BGC land surface model. Sixteen Earth System Models (ESMs) from CMIP5 and CMIP6 were employed to supply the variables of climate change under low, middle, and high greenhouse emission scenarios in the 1.5 °C and 2.0 °C scenarios. The ensemble means from the FFDIn and results from the CLM4.5-BGC with multiple forcings show that the dry areas in the southwestern US, Brazilian Highlands, and Arabian islands are projected to face higher wildfire risk with larger burned areas and more carbon emissions under a warmer climate. The Congo Basin and part of the Amazon could have a lower wildfire risk with smaller burned areas and less carbon emissions. The absolute changes in the projected FFDIn are small, although large increases are observed in boreal areas, particularly in the winter and spring. Burned area and carbon emissions are projected to increase in general in the boreal area but decrease in northeastern Asia. Compared to the 1.5 °C scenario, the wildfire risk and burned area levels are projected to increase under the 2.0 °C scenario except in the western Amazon. However, fire carbon emissions are projected to decrease more in tropical areas under the 2.0 °C scenario. The different change directions in eastern North America and eastern China produced by the FFDIn and CLM4.5-BGC suggest the potential effect of non-meteorological elements on fire activities.
Arid and semi-arid regions of the world are particularly vulnerable to greenhouse gas–driven hydroclimate change. Climate models are our primary tool for projecting the future hydroclimate that society in these regions must adapt to, but here, we present a concerning discrepancy between observed and model-based historical hydroclimate trends. Over the arid/semi-arid regions of the world, the predominant signal in all model simulations is an increase in atmospheric water vapor, on average, over the last four decades, in association with the increased water vapor–holding capacity of a warmer atmosphere. In observations, this increase in atmospheric water vapor has not happened, suggesting that the availability of moisture to satisfy the increased atmospheric demand is lower in reality than in models in arid/semi-arid regions. This discrepancy is most clear in locations that are arid/semi-arid year round, but it is also apparent in more humid regions during the most arid months of the year. Here, it indicates a major gap in our understanding and modeling capabilities which could have severe implications for hydroclimate projections, including fire hazard, moving forward.
This report details model development, theory, and a literature review focusing on the emission of contaminants on solid substrates in fires. This is the final report from a 2-year Nuclear Safety Research and Development (NSRD) project. The work represents progress towards a goal of having modeling and simulation capabilities that are sufficiently mature and accurate that they can be utilized in place of physical tests for determining safe handling practices. At present, the guidelines for safety are largely empirically based, derived from a survey of existing datasets. This particular report details the development, verification and calibration of a number of code improvements that have been implemented in the SIERRA suite of codes, and the application of those codes to three different experimental scenarios that have been subject of prior tests. The first scenario involves a contaminated PMMA slab, which is exposed to heat. The modeling involved a novel method for simulating the viscous diffusion of the particles in the slab. The second scenario involved a small pool fire of contaminated combustible liquid mimicking historical tests and finds that the release of contaminants has a high functionality with the height of the liquid in the container. The third scenario involves the burning of a contaminated tray of shredded cellulose. A novel release mechanism was formulated based on predicted progress of the decomposition of the cellulose, and while the model was found to result in release that can be tuned to match the experiments, some modifications to the model are desirable to achieve quantitative accuracy.
Abstract Understanding the complex interrelationships between wildfire and its environmental and anthropogenic controls is crucial for wildfire modeling and management. Although machine learning (ML) models have yielded significant improvements in wildfire predictions, their limited interpretability has been an obstacle for their use in advancing understanding of wildfires. This study builds an ML model incorporating predictors of local meteorology, land‐surface characteristics, and socioeconomic variables to predict monthly burned area at grid cells of 0.25° × 0.25° resolution over the contiguous United States. Besides these predictors, we construct and include predictors representing the large‐scale circulation patterns conducive to wildfires, which largely improves the temporal correlations in several regions by 14%–44%. The Shapley additive explanation is introduced to quantify the contributions of the predictors to burned area. Results show a key role of longitude and latitude in delineating fire regimes with different temporal patterns of burned area. The model captures the physical relationship between burned area and vapor pressure deficit, relative humidity (RH), and energy release component (ERC), in agreement with the prior findings. Aggregating the contribution of predictor variables of all the grids by region, analyses show that ERC is the major contributor accounting for 14%–27% to large burned areas in the western US. In contrast, there is no leading factor contributing to large burned areas in the eastern US, although large‐scale circulation patterns featuring less active upper‐level ridge‐trough and low RH two months earlier in winter contribute relatively more to large burned areas in spring in the southeastern US.
Ejector driven systems have the ability to operate at high efficiencies, utilizing recycled thermal energy as a power source. For a typical ejector heat pump system, the increase of the condenser temperature reduces the coefficient of performance (COP). In addition, if the condenser temperature is higher than the critical temperature, the ejector may not function. In this situation, the condenser temperature must be reduced, and an additional heater will be utilized to heat the production water from the condenser temperature to the desired temperature. In this investigation, a single-stage gas-fired ejector heat pump (EHP) is investigated and thermodynamically modeled in order to optimize the system COP for the purpose of heating water by utilizing the thermal energy from the ambient air. The effects of the high-temperature evaporator (HTE) and low-temperature evaporator (LTE) temperatures on the ejector critical back pressure and the EHP system performance are examined for a HTE temperature range of 120–180 °C and LTE temperatures of 15.5, 17.5, and 19.5 °C. Results show that an optimized COP of the EHP system exists which depends on HTE and LTE temperatures, primary nozzle throat diameters. In addition, it is found that the EHP COP is independent of the ejector COP. From this investigation a maximum EHP COP of 1.31 is able to be achieved for a HTE temperature of 160 °C and a LTE temperature of 19.5 °C with a total heat capacity of 15.98 kW.
The primary objective of this project was to develop and demonstrate an integrated pilot-scale circuitry for recovering high-value rare earth elements (REEs) from coal and coal byproducts. The target performance was to produce a mixed REE product with content of at least two percent by weight on a dry mass basis in a cost-effective and environmentally benign manner. During the first nine months of the project period (Phase 2 Budget Period 2), pilot plant construction was completed including all field site startup activities such as permitting, engineering design, procurement/bidding, unit fabrication, site construction, equipment installation, module assembly, safety training, and circuit shakedown. During the remaining 21 months of project period (Phase 2 Budget Period 3), detailed field-testing activities were performed including feedstock sample collection and preparation, exploratory testing, circuit modification, detailed parametric study, and performance optimization. A detailed techno-economic analysis was performed based on the pilot plant testing findings which provided various scenarios for REE production. The project successfully accomplished the proposed target performance by producing mixed rare earth oxide (REO) with greater than 90% purity by weight in a continuous pilot scale operation from two distinctly different coarse refuse materials (i.e., West Kentucky No. 13 and Fire Clay coal seams), and at least three secondary sources (i.e., heap leach process and naturally formed acid mine drainage system). Project partners included the University of Kentucky, Virginia Tech, West Virginia University, Alliance Coal, Blackhawk Mining, Mineral Refining Company, and Mineral Separation Technologies. The pilot scale test facility was constructed at a former mining complex owned by Alliance Natural Resource Partners (Alliance Coal). The site was rehabilitated to accommodate the equipment installation, construction and fabrication, electrical power requirement, water line management and containment. The process units constructed and installed included X-ray sorting unit, crushing and grinding unit, physical separation unit, acid leaching unit, solvent extraction unit, and wastewater management unit. A rare earth mineral concentration unit was constructed as a standalone unit for flexible operation. A detailed environmental assessment and control plan was carried out to identify and quantify any potential impacts of the pilot-scale processing circuitry on the human and eco-system health and well-being. Corresponding mitigation strategies and control measures were provided. A conceptual flowsheet was developed to effectively remove thorium and uranium from high purity rare earth oxide mix or any potential radionuclide enriched stream. The two distinct feedstock materials were secured from the Blackhawk Mining Complex in eastern Kentucky where the Fire Clay (Hazard No. 4) seam is processed. The West Kentucky No. 13 (Baker) coarse refuse material was collected from an active process stream at an Alliance coal preparation plant located in western Kentucky. Characterization analysis indicated that both of feed materials generated from the two sources contained >300 ppm of TREEs on a dry whole mass basis which met the requirements for a qualified feed stock. The two feedstocks were further upgraded using a dual x-ray sorter to prepare the feed material for hydrometallurgical circuit. Thermal treatment on feed material prior to leaching was found to: 1) improve the leaching recovery of REEs, 2) increase the leaching kinetics, and 3) allow the leaching reaction to occur at lower acidity. Roasting at 600°C was selected as the pre-treatment condition for both West Kentucky No. 13 and Fire Clay coarse refuse material. Over 40% of leaching recovery was achieved by roasting West Kentucky No. 13 material having a top particle size of 3 mm in the pilot scale operation using 1.2M sulfuric acid leaching at 75OC. Initial pilot scale testing involved continuous operation of the pilot plant for 94 hours. The leaching unit was operated at solid-to-liquid ratio of 1 to 10 (w/v) using 0.5M sulfuric acid solution at a temperature of 75°C. The continuous solvent extraction circuit utilized rougher and cleaner units with DEHPA and TBP as the extractants. An innovative stripping circuit was developed to accumulate the REE concentration in the stripping solution to a level above 600 ppm. A bleed stream from the recycled strip solution was treated using oxalic acid precipitation which produced a high grade rare earth oxalate. The oxalate product was roasted to remove the oxalate which produced a rare earth oxide product having a purity greater 90%. Due to high concentrations of contaminant ions in the pregnant leach solution (PLS), a modified flowsheet was developed that involved pre-concentration of the REEs using multiple stages of precipitation and redissolution. The advantage of this process was improved removal of contamination before the downstream purification process and a significant cost reduction relative to the circuit that utilized the solvent extraction process. The modified circuitry included processes involving leaching, multistage precipitation, redissolution, and oxalate precipitation followed by roasting of the oxalate product. The circuit produced a mixed REO that was 92.96% pure from the initial test. A detailed parametric test plan was carried out which involved varying key parameters including solids feed rate, acid flowrate, acid concentration, multistage precipitation pH, redissolution pH, oxalate precipitation dosage and pH. The response variables included REE recovery, contaminant recovery, REO product grade and overall chemical consumption. Test results indicated that the acid-to-solid ratio is the key parameter to leaching efficiency as performance deteriorated with an increase in solids concentration. The optimal pH determined for REE precipitation and redissolution was 6.5 and 2.5, respectively. Additional tests were conducted to further improve the flowsheet. Recirculating a portion of the PLS to the feed of the leach tanks improved the leaching performance by lowering the pH of the leaching system and reducing the contamination recovery by shortening the residence time. Moreover, the removal of Al prior to REE precipitation significantly reduced the oxalic acid consumption in the oxalate precipitation circuit. The modified circuit produced over 90% grade REO by weight from both West Kentucky No. 13 and Fire Clay coarse refuse material in pilot scale continuous test programs. A case analysis model was developed to project the REE and major contaminants concentration in each PLS stream based on the leaching condition, pH cut point, and oxalic acid dosage. A correlation was established using empirical and semi-empirical models. Using the models, chemical consumption required for each stage was predicted based on the projected performance of the hydrometallurgy circuit. After identifying the optimum conditions, validation tests were carried out for the treatment of both West Kentucky No. 13 and Fire Clay coarse refuse materials in the pilot plant. The actual circuit performance and chemical consumptions were very close to the model predictions. Other than the two coarse refuse sources, several secondary feedstocks were also tested in the pilot plant facility. A “heap leach” system was constructed using the coarse refuse material generated from cleaning the West Kentucky No. 13 seam coal. Using the two stage SX rougher and cleaner circuit, a concentrate with a grade >90% REO was produced while recovering >97% of the REEs from the heap leach PLS. Naturally generated acid mine drainage (AMD) from West Kentucky No.13 mine was processed using the multistage precipitation circuit in the pilot plant in a test conducted for a period of 32 hours. The final grade of the mix RE oxide produced from the AMD was 90.84% with an overall circuit recovery of 64%. The primary source of REE was the selective precipitation steps involving iron and aluminum rejection. The hydrophobic-hydrophilic separation (HHS) process was proven to effectively recover coal from fine waste materials. For REM recovery, the HHS process was able to produce concentrates at grades of approximately 1.8% REE on an ash basis; however, recovery values were typically low, <10%, under the optimal conditions determined in the laboratory-scale testing. Staged testing of the pilot-scale HHS process for coal recovery and semi-continuous laboratory testing for REM testing showed that a total concentration ratio of more than 15x was observed for the REM recovery process. A circuit simulation package was developed for REE extraction and purification using a spreadsheet-based platform (Microsoft Excel). The REESim circuit simulation package is configured to track the mass and volume flows of components passing through a series of unit operations specified and configured by the user. The mass rates can then be utilized by the user to determine important performance indicators such as product mass yields, concentrate purity levels, element-by-element recoveries, and so forth. The techno-economic analysis showed that the roasting and leaching operations were the most expensive capital items, each contributing approximately 30% to the total capital cost. One notable contributor to the high production costs was the low REE recovery observed in the pilot scale trials. The product basket price was shown to have a strong influence on the economic viability of the scenarios, with the scandium price being the most significant influencer. Operating cost was shown to be extremely sensitive to REE recovery, REE feed grade, and leaching acid consumption. An analysis of ten different scenarios for a 500 t/h commercial operation revealed that three were economically favorable, producing internal rates of return varying from 27.7% to 33.1% and payback periods of 4 to 5 years. The project successfully developed and demonstrated a process to recover REEs from coal and coal byproducts in a pilot-plant operation which consistently produced over 90% grade REO mix from varies types of feedstocks. Commercialization analysis showed that the technology readiness level successfully achieved TRL 6 at the end of the project and demonstrated the need and the potential for scaling the process to further advance the technologies toward the goal of providing a domestic supply of REEs at a commercial scale.
Aerial application of fire retardant is a critical tool for managing wildland fire spread. Retardant applications are carefully planned to maximize fire line effectiveness, improve firefighter safety, protect high-value resources and assets, and limit environmental impact. However, topography, wind, visibility, and aircraft orientation can lead to differences between planned drop locations and the actual placement of the retardant. Information on the precise placement and areal extent of the dropped retardant can provide wildland fire managers with key information to (1) adaptively manage event resources, (2) assess the effectiveness of retardant slowing or stopping fire spread, (3) document location in relation to ecologically sensitive areas; and perform or validate cost-accounting for drop services. This study uses Sentinel-2 satellite data and commonly used machine learning classifiers to test an automated approach for detecting and mapping retardant application. We show that a multiclass model (retardant, burned, unburned, and cloud artifact classes) outperforms a single-class retardant model and that image differencing (post-application minus pre-application) outperforms single-image models. Compared to the random forest and support vector machine, the gradient boosting model performed the best with an overall accuracy of 0.88 and an F1 Score of 0.76 for fire retardant, though results were comparable for all three models. Our approach maps the full areal extent of the dropped retardant within minutes of image availability, rather than linear representations currently mapped by aerial GPS surveys. The development of this capability allows for the rapid assessment of retardant effectiveness and documentation of placement in relation to sensitive environments.
Focus area: Primary focal area #2; secondary focal area #3: Learning about parameters and processes of land surface hydrologic and biogeochemical models in Earth System models by integrating machine learning, physics, and big data. Science challenges: How do we maximally leverage big-data observations to improve hydrobiogeochemical process description and parameterization so that such modules more realistically capture hydrologic and vegetation responses and feedbacks under the future climate? For example, how can we leverage physics, limited observations of vegetation and streamflow to better estimate evapotranspiration, and, relatedly, net primary productivity, especially for drought areas? Vegetation plays a critical role in regional and global water cycles; however, existing vegetation models have failed to predict vegetation response to droughts (McDowell & Xu, 2017) , arctic greening (Keenan & Riley, 2018) , and critical transitions between forest and savanna (Hirota et al., 2011) . These studies suggest that when we build process-based models (PBM) parameterized from regional and global plant traits, we tend to poorly describe plant adaptation and local-scale competition processes. The models and their associated parameters assigned for different regions in the world are not capturing essential heterogeneity in vegetation responses at finer spatial scales. Many parameters of the land surface models control hydrology and vegetation dynamics at the same time. The heterogeneity in vegetation response is a function of (i) plant type, (ii) plant size, (iii) competition and succession, (iv) environmental controls, and (v) local variations due to the unique ecological community that are very difficult to describe (e.g., the size of gaps resulting from fire that facilitated the coexistence of pioneering species). In the demographic models, only factors (i) and (iv) were captured, and plant types were generally described only by leaf phenology and climate zones. With current demographic models, we generally consider more traits to define plant types (i) and calibrate these traits to consider factors (ii), (iii) and (iv); however, it is substantially challenging to scale to regional and global simulations due to trait variations across space (Ali et al., 2016). Moreover, it has been noted that hillslope processes, including ridge-to-valley flow and sunny vs. shady slopes are primary organizers of water, energy, and vegetation (Clark et al., 2015; Fan et al., 2019) . Although gradual improvements in the hydrologic model component in earth system models may reduce this error (at a remarkably slow pace), the long-term, gradual impact of hydrology on plant traits are not well captured. Recent work showed that the hydrologic controls exerted by groundwater and lateral flow are primary regulators of rooting depth (Fan et al., 2017) . Such hydrologic controls have seldom been reflected in vegetation model parameterizations.