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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

A Comparison of Snow Depth on Sea Ice Retrievals Using Airborne Altimeters and an AMSR-E Simulator

A comparison of snow depths on sea ice was made using airborne altimeters and an Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) simulator. The data were collected during the March 2006 National Aeronautics and Space Administration (NASA) Arctic field campaign utilizing the NASA P-3B aircraft. The campaign consisted of an initial series of coordinated surface and aircraft measurements over Elson Lagoon, Alaska and adjacent seas followed by a series of large-scale (100 km ? 50 km) coordinated aircraft and AMSR-E snow depth measurements over portions of the Chukchi and Beaufort seas. This paper focuses on the latter part of the campaign. The P-3B aircraft carried the University of Colorado Polarimetric Scanning Radiometer (PSR-A), the NASA Wallops Airborne Topographic Mapper (ATM) lidar altimeter, and the University of Kansas Delay-Doppler (D2P) radar altimeter. The PSR-A was used as an AMSR-E simulator, whereas the ATM and D2P altimeters were used in combination to provide an independent estimate of snow depth. Results of a comparison between the altimeter-derived snow depths and the equivalent AMSR-E snow depths using PSR-A brightness temperatures calibrated relative to AMSR-E are presented. Data collected over a frozen coastal polynya were used to intercalibrate the ATM and D2P altimeters before estimating an altimeter snow depth. Results show that the mean difference between the PSR and altimeter snow depths is -2.4 cm (PSR minus altimeter) with a standard deviation of 7.7 cm. The RMS difference is 8.0 cm. The overall correlation between the two snow depth data sets is 0.59.

Cavalieri, D. J.↗
NASA NTRS2013

Improved Boundary Layer Depth Retrievals from MPLNET

Continuous lidar observations of the planetary boundary layer (PBL) depth have been made at the Micropulse Lidar Network (MPLNET) site in Greenbelt, MD since April 2001. However, because of issues with the operational PBL depth algorithm, the data is not reliable for determining seasonal and diurnal trends. Therefore, an improved PBL depth algorithm has been developed which uses a combination of the wavelet technique and image processing. The new algorithm is less susceptible to contamination by clouds and residual layers, and in general, produces lower PBL depths. A 2010 comparison shows the operational algorithm overestimates the daily mean PBL depth when compared to the improved algorithm (1.85 and 1.07 km, respectively). The improved MPLNET PBL depths are validated using radiosonde comparisons which suggests the algorithm performs well to determine the depth of a fully developed PBL. A comparison with the Goddard Earth Observing System-version 5 (GEOS-5) model suggests that the model may underestimate the maximum daytime PBL depth by 410 m during the spring and summer. The best agreement between MPLNET and GEOS-5 occurred during the fall and they diered the most in the winter.

Micropluse Lidar Network↗
DOE OSTI2016

Nonextensive statistics and skin depth of transverse wave in collisional plasma

Skin depth of transverse wave in a collisional plasma is studied taking into account the nonextensive electron distribution function. Considering the kinetic theory for charge particles and using the Bhatnagar-Gross-Krook collision model, a generalized transverse dielectric permittivity is obtained. The transverse dispersion relation in different frequency ranges is investigated. Obtaining the imaginary part of the wave vector from the dispersion relation, the skin depth for these frequency ranges is also achieved. Profiles of the skin depth show that by increasing the q parameter, the penetration depth decreases. In addition, the skin depth increases by increasing the electron temperature. Finally, it is found that in the high frequency range and high electron temperature, the penetration depth decreases by increasing the collision frequency. In contrast, by increasing the collision frequency in a highly collisional frequency range, the skin depth of transverse wave increases.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Lower bounds on circuit depth of the quantum approximate optimization algorithm

The quantum approximate optimization algorithm (QAOA) is a method of approximately solving combinatorial optimization problems. While QAOA is developed to solve a broad class of combinatorial optimization problems, it is not clear which classes of problems are best suited for it. One factor in demonstrating quantum advantage is the relationship between a problem instance and the circuit depth required to implement the QAOA method. As errors in noisy intermediate-scale quantum (NISQ) devices increase exponentially with circuit depth, identifying lower bounds on circuit depth can provide insights into when quantum advantage could be feasible. In this work, we identify how the structure of problem instances can be used to identify lower bounds for circuit depth for each iteration of QAOA and examine the relationship between problem structure and the circuit depth for a variety of combinatorial optimization problems including MaxCut and MaxIndSet. Specifically, we show how to derive a graph, G, that describes a general combinatorial optimization problem and show that the depth of circuit is at least the chromatic index of G. By looking at the scaling of circuit depth, we argue that MaxCut, MaxIndSet, and some instances of vertex covering and Boolean satisfiability problems are suitable for QAOA approaches while knapsack and traveling salesperson problems are not.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Temperature sensitivity of extracellular enzymes differs with peat depth but not with season in an ombrotrophic bog

Peatlands contain a large portion of Earth's terrestrial soil organic matter in part due to a reduction in decomposition rates. Organic matter decomposition is initially mediated by extracellular enzyme activity, which is in turn controlled by temperature, moisture, and substrate availability; and all are subject to seasonal variation. As depth increases in peatlands, temperature variability and labile carbon inputs decrease. We hypothesized that the more stable recalcitrant subsurface would contain a smaller less diverse enzyme pool, that is better adapted to a narrow temperature range. Thus temperature dependence would be diminished at depth compared to superficial peat. Potential enzyme activity rates were determined across seasons and with depth in peat samples collected from the Marcell Experimental Forest in northern Minnesota, USA. The temperature dependence, assessed by activation energy, was quantified for three hydrolytic enzymes involved in nutrient cycling at up to 15 temperature points ranging from 2 °C to 65 °C. Potential enzyme activity decreased with peat depth as expected and corresponded with changes in peat composition and microbial biomass from the acrotelm to the catotelm. In an environmentally relevant temperature range (2–23 °C), activation energy decreased with depth for β-glucosidase as predicted and leucine amino peptidase activation energy was the lowest of all enzymes. Stable temperatures at depth appear to result in a microbial community containing enzymes that have lower sensitivity to temperature increases. Surprisingly, there was no significant seasonal effect on enzyme temperature dependence observed in our study. Based on these results, and without shifts in microbial community composition, warming of peat could result in increased carbon and phosphorus cycling at the surface but little change at depth. As a result, differences in enzyme temperature sensitivity suggest nitrogen cycling could remain constant with warming, potentially resulting in proteolytic nitrogen cycling being decoupled from carbon and phosphorus cycling.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Imperfect slope measurements drive overestimation in a geometric cone model of lake and reservoir depth

Lake and reservoir (waterbody) depth is a critical characteristic that influences many important ecological processes. Unfortunately, depth measurements are labor-intensive to gather and are only available for a small fraction of waterbodies globally. Therefore, scientists have tried to predict depth from characteristics easily obtained for all waterbodies, such as surface area or the slope of the surrounding land. One approach for predicting waterbody depth simulates basins using a geometric cone model where the nearshore land slope and distance to the center of the waterbody are assumed to be representative proxies for in-lake slope and distance to the deepest point respectively. We tested these assumptions using bathymetry data from ~5000 lakes and reservoirs to examine whether differences in waterbody type or shape influenced depth prediction error. Here, we found that nearshore land slope was not representative of in-lake slope, and using it for prediction increases error substantially relative to models using true in-lake slope for all waterbody types and shapes. Predictions were biased toward overprediction in concave waterbodies (i.e., bowl-shaped; up to 18% of the study population) and reservoir waterbodies (up to 30% of the study population). Despite this systematic overprediction, model errors were fewer (in absolute and relative terms, irrespective of any specific slope covariate) for concave than convex waterbodies, suggesting the geometric cone model is an adequate representation of depth for these waterbodies. But because convex waterbodies are far more common (>72% of our study population), minimizing overall depth prediction error remains a challenge.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

SU-G-TeP1-10: Predicting Photon Percentage Depth Dose From Profile Due to Changes in Beam Quality

Purpose: To predict photon percentage depth dose (PDD) from profile due to a change in flattened (FF) and flattening-filter-free (FFF) beam quality. Methods: 6MV photon beam PDDs and profiles in a 3D water tank (3DW) and profiles in an ionization chamber array (ICP) were collected for different field sizes and depths with FF and FFF beams in a Versa HD (Elekta Ltd.). The energy was adjusted by changing the bending magnet current (BMC) ±15% from the clinical beam (6MV) in 5% increments. For baseline establishment, PDDs(depth≥3cm) were parameterized with bi-exponential functions and the PDD 20 to 10cm ratios (PDD{sub 20,10}) were calculated. Then, the FF profile at 10cm from the central axis (Pr{sub 10}) and the slope of the FFF central linear region (SFFF) were calculated. Calibration curves were established: (1) change in Pr{sub 10} and SFFF as functions of the change in PDD{sub 20,10} and (2) change in PDD(depth=3, 15 and 30cm) as function of the change in PDD{sub 20,10}. The differences between Pr{sub 10} and SFFF from baseline were calculated and, from calibration curves, changes in PDD{sub 20,10} and PDD(depth=3, 15 and 30cm) were obtained. Then, absolute PDD(depth=3, 15 and 30cm) values were input into a least-square-optimization algorithm to calculate the bi-exponential function’s optimal coefficients and generate the PDD(depths≥3cm). Results: The change in PDD{sub 20,10} relative to baseline increased (<±4%) with BMC. Pr{sub 10} increased (±6%) and SFFF decreased (±11%) with BMC. Relative differences between measured and calculated (i.e. PDD calculation from Pr{sub 10} and SFFF) PDDs were less than 1%. Results apply to FF and FFF beams measured in 3DW and ICP. Conclusion: Pr{sub 10} and SFFF are more sensitive than PDD to changes in beam energy and PDD information can be accurately generated from them. With known 3DW and ICP profile relationship, ICP can be used to obtain PDD for current photon beam.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2024

Continuous snow depth, ground interface temperature and shallow soil temperature measurements from 2021-10-1 to 2022-6-14, Seward Peninsula, Alaska

The dataset contains co-located snow depth, ground interface temperature, and shallow soil temperature measured at 98 discrete locations in a watershed located along the Nome-Teller road at mile marker 27 (referred to as T27), and at 53 discrete locations on a hillslope located along the Kougarok road at mile marker 64 (referred to as K64), in Seward Peninsula, Alaska. The dataset aims to understand the local heterogeneity of snow depth, snow temperature, and soil temperature dynamics and their interactions in a discontinuous permafrost region. The dataset is also valuable to train and evaluate machine learning and physical models to predict snow depth or the impact of snow depth on ground surface temperature. At each location, temperatures above and below the ground surface were measured by a pair of vertically deployed distributed temperature profiling probes designed based on Dafflon et al (2022). The probes have high precision temperature sensors spaced at 5 or 10 cm. The mean daily snow depth was estimated by identifying the pair of consecutive sensors with maximum drop of daily temperature high-frequency fluctuations. The ground interface temperature was measured by the sensor located 1-5 cm above the ground surface at 15-minute intervals. The shallow soil temperature was measured by the sensor located 1-5 cm below the ground surface at 15-minute intervals. The dataset includes a description of the probe locations in the "Probe_locations_*.csv" file and the 3 data files (Snow_depths_*.csv, Ground_interface_temperatures_*.csv, Shallow_soil_temperatures_*.csv). * is either T27 or K64, which are the two study sites. In each data file, each column corresponds to a measurement location. Metadata files include data descriptions (_dd.csv) for tabular data. All included files are listed and described in xxxx_flmd.csv. A more detailed description of data processing, along with an updated dataset incorporating multiple seasons and improved snow depth estimation is available at https://doi.org/10.15485/2480365 (Wang et al., 2025a, Wang et al., 2025b).The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Daily Snow Depth Measurements from 195 Stations in the United States (1997) (NDP-059)

This data package provides daily measurements of snow depth at 195 National Weather Service (NWS) first-order climatological stations in the United States. The data have been assembled and made available by the National Climatic Data Center (NCDC) in Asheville, North Carolina. The 195 stations encompass 388 unique sampling locations in 48 of the 50 states; no observations from Delaware or Hawaii are included in the database. Station selection criteria emphasized the quality and length of station records while seeking to provide a network with good geographic coverage. Snow depth at the 388 locations was measured once per day on ground open to the sky. The daily snow depth is the total depth of the snow on the ground at measurement time. The time period covered by the database is 1893-1992; however, not all station records encompass the complete period. While a station record ideally should contain daily data for at least the seven winter months (January through April and October through December), not all stations have complete records. Each logical record in the snow depth database contains one station's daily data values for a period of one month, including data source, measurement, and quality flags. The snow depth data have undergone extensive manual and automated quality assurance checks by NCDC and the Carbon Dioxide Information Analysis Center (CDIAC). These reviews involved examining the data for completeness, reasonableness, and accuracy, and included comparison of some data records with records in NCDC's Summary of the Day First Order online database. Since the snow depth measurements have been taken at NWS first-order stations that have long periods of record, they should prove useful in monitoring climate change.For access to the data files, click this link to the CDIAC data transition website: http://cdiac.ess-dive.lbl.gov/ndps/ndp059.html

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2020

Arctic Snow Depth and Sea Ice Thickness From ICESat‐2 and CryoSat‐2 Freeboards: A First Examination

We present a first examination of Arctic sea ice snow depth estimates from differencing satellite lidar (ICESat‐2) and radar (CryoSat‐2) freeboards. These estimates cover the period between 14 October 2018 and the end of April 2019. Snow depth is related to freeboard differences by the refractive index/bulk density of the snow layer—the only free parameter in the approach. Area‐averaged snow depth ranges from 9 cm (on first‐year ice: 5 cm, multiyear ice: 14 cm) in late October to 19 cm (first‐year ice: 17 cm, multiyear ice: 27 cm) in April; on average, this snow is thinner over FYI. Spatial patterns and gradients of snow depth estimates compare well with reconstructions using snowfall from ERA‐Interim and ERA5, although snowfall from ERA5 is systematically higher. For all months, the results suggest that ~50% of the total freeboard is comprised of snow. Retrievals are within a few centimeters of snow depth data acquired by Operation IceBridge in April 2019. Sources of uncertainties associated with this freeboard‐differencing approach are discussed. Further, sea ice thicknesses calculated using the retrieved snow depth and a modified climatology are contrasted. Comparatively, the snow depth and calculated ice thickness using a modified climatology are higher by ~5 cm and 0.33 m, although these differences are not uniform throughout the season. Snow accumulation was slower between October and December but increased between December and January, unlike the modified climatology, which exhibited a monotonic accumulation for all months. Future opportunities for assessment and improvement of these estimates are discussed.

Arctic Ocean↗
NASA NTRSDate not supplied

How Much Blue Carbon do Hudson Estuary Marshes Sequester? A Full-Depth Carbon Stock Estimation of Iona Marsh, Lower Hudson, NY

Tidal marshes function as a key ecosystem, by serving as nurseries, improving water quality, protecting biodiversity, preventing erosion of coastlines, and preserving archives of paleoclimate and paleoecology. In the last few decades, researchers have come to appreciate coastal marshes’ carbon sequestration function. However, full peat depth is required for estimating the true carbon content of marshes, and very little research has been done to discover the full peat depth along coastlines. We selected Iona Marsh (73°58’44.972”W, 41°18’6.366”N), situated adjacent to Bear Mountain Park and part of the National Estuarine Research Reserve, to document the belowground storage of blue carbon. In order to calculate marsh volume, three full-depth transects were performed from 2020 to 2022, two from east to west in different regions of the marsh, and one from north to south at a minimum of 10 m intervals. We found that marsh depth ranged from 5.1 m to 13.2 m. We calculated marsh volume from the full peat depth and marsh area, obtained through the National Wetlands Inventory database, by applying the average marsh depth over its entire surface area. In order to calculate carbon content, we extracted a 9m sediment core and used loss-on-ignition (LOI) to determine organic matter, then estimate carbon content as half of the organic matter. We used 198 LOI samples to calculate the carbon, which formed at this site over 6000 years ago. We intend to apply this methodology framework to other marshes in the Hudson region. Because many previous carbon stock estimates only account for the top 1 m of peat, the role of these ecosystems in carbon sequestration is significantly underestimated. By determining the full-depth carbon stock of these marshes, we hope to emphasize the value of marshes to the community, beyond what is traditionally recognized, particularly in light of sea level rise and the vulnerability of marshes to erosion.

Tidal marshes↗
DOE OSTI2015

An empirical study of the distribution of earthquakes with respect to rock type and depth

Whether fault slip occurs seismically or aseismically depends on the frictional properties of the fault, which might be expected to depend on rock type and depth, as well as other factors. To view the effect of rock type and depth on the distribution of earthquakes, we compare geologic models of the San Francisco Bay and the Southern California regions to the distribution of seismicity. We normalize the number of earthquakes within each rock type and depth interval by the corresponding volume to determine the earthquake density. Earthquake density is confirmed primarily by depth, while whether the rock is sedimentary or basement has only a secondary, depth-dependent effect on the earthquake density. At very shallow depths, there is no difference in earthquake density between sedimentary and basement rocks. The earthquake density of basement rocks increases with depth more rapidly than that of sedimentary rocks to a similar but shallower maximum.

3‐D geologic models↗
DOE OSTI2017

Soil thermal dynamics, snow cover, and frozen depth under five temperature treatments in an ombrotrophic bog: Constrained forecast with data assimilation: Forecast With Data Assimilation

We report that accurate simulation of soil thermal dynamics is essential for realistic prediction of soil biogeochemical responses to climate change. To facilitate ecological forecasting at the Spruce and Peatland Responses Under Climatic and Environmental change site, we incorporated a soil temperature module into a Terrestrial ECOsystem (TECO) model by accounting for surface energy budget, snow dynamics, and heat transfer among soil layers and during freeze-thaw events. We conditioned TECO with detailed soil temperature and snow depth observations through data assimilation before the model was used for forecasting. The constrained model reproduced variations in observed temperature from different soil layers, the magnitude of snow depth, the timing of snowfall and snowmelt, and the range of frozen depth. The conditioned TECO forecasted probabilistic distributions of soil temperature dynamics in six soil layers, snow, and frozen depths under temperature treatments of +0.0, +2.25, +4.5, +6.75, and +9.0°C. Air warming caused stronger elevation in soil temperature during summer than winter due to winter snow and ice. And soil temperature increased more in shallow soil layers in summer in response to air warming. Whole ecosystem warming (peat + air warmings) generally reduced snow and frozen depths. The accuracy of forecasted snow and frozen depths relied on the precision of weather forcing. Uncertainty is smaller for forecasting soil temperature but large for snow and frozen depths. Lastly, timely and effective soil thermal forecast, constrained through data assimilation that combines process-based understanding and detailed observations, provides boundary conditions for better predictions of future biogeochemical cycles.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Modelling the response of vegetation restoration to changes in groundwater level, based on ecologically suitable groundwater depth

Groundwater-level fluctuations at a large scale have a significant effect on the preservation and restoration of vegetation. This study determined suitable groundwater depth within which natural vegetation grows well, and analysed the effect of groundwater regulation on vegetation restoration in Tianjin City, northern China. Normal and lognormal distributions were used to fit the curve of the relation between vegetation and groundwater depth. The groundwater depth range corresponding to the higher frequency of vegetation distribution was regarded as the ‘suitable water depth’ range for vegetation growth. The suitable groundwater depth for shrub growth was 3–5 m and for grass growth 1–3 m. A groundwater flow model predicted the future changes of groundwater depths in the vegetation distribution area under the condition that the current levels of groundwater extraction are maintained. The results showed that there is potential for the extraction of groundwater in shrubland areas, but for grassland areas the water-table elevation showed a downward trend, meaning that water shortages in some areas may be more severe in the future. Finally, based on the current groundwater extraction regime, two regulation schemes were developed: (1) for shrubland, groundwater extraction was reduced by 10% in the ecological water deficit areas, and extraction was increased by 10% in the ecological water surplus and suitable areas, and (2) for grassland, groundwater recharge was increased by the restoration of the wetland areas. In conclusion, the groundwater depths in most of the area would be more suitable for vegetation growth under the regulation schemes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Multi-depth suspended sediment estimation using high-resolution remote-sensing UAV in Maumee River, Ohio

Satellite remote-sensing has been widely used to map suspended sediment concentration (SSC) in waterbodies. Current development of the unmanned aerial vehicle (UAV) technology allows mapping of SSC at finer spatial resolution providing high flexibility in terms of cost and acquisition time. However, the technology is immature and transfer of empirical algorithms from existing remote-sensing technologies to UAV still has to be explored. Here, this study uses the MicaSense Sequoia sensor with four bands (green, red, red edge, and near-infrared [NIR]) mounted on-board a fixed-wing UAV to map SSC within the Maumee River in Ohio, USA, at multiple depth intervals (15, 61, 91, and 182 cm). The simple linear and stepwise regression models show the advantage of multiple bands and band ratios over single bands in mapping SSC. The findings show a limited performance of the Sequoia sensor when compared to field spectroradiometer measurements. In all cases but one, the adjusted coefficient of determination (R$2\atop{adj}$) values is lower for the UAV data. The regression equations become similar at and below a depth of 0–61 cm, and R$2\atop{adj}$ become constant at and below a depth of 0–91 cm. While the spectroradiometer-related equations are sensitive to a wider spectral range (from green at the surface to NIR wavelength at 182 cm depth), the UAV-related equations are insensitive to green spectrum and they include a narrower spectral range (from red to NIR) over all depth increments. Field spectroradiometer measurements exhibit a strong relationship with cumulative SSC at 182 cm depth (0–182 cm) (R$2\atop{adj}$ = 0.71) whereas UAV reflectance data show the best relationship with SSC at 91 cm (0–91 cm) (R$2\atop{adj}$ = 0.56) suggesting that ~91 cm may be an optimal depth for UAV under given conditions. In conclusion, the results show that UAVs can be a practical but somewhat limited tool to monitor SSC in small- to medium-sized rivers.

47 OTHER INSTRUMENTATION↗
DOE OSTI2016

SU-F-T-277: Using Dose Weighted Mean Effective Depth to Improve Accuracy of Secondary MU Calculation for VMAT Plans

Purpose: In commercial secondary dose calculation system, an average effective depth is used to calculate the Monitor Units for an arc beam from the volumetric modulated arc (VMAT) plans. Typically, an arithmetic mean of the effective depths (AMED) of a VMAT arc beam is used, which may result in large MU discrepancy from that of the primary treatment planning system. This study is to demonstrate the use of a dose weighted mean effective depth (DWED) can improve accuracy of MU calculation for the secondary MU verification. Methods: In-house scripts were written in the primary treatment planning system (TPS) to first convert a VMAT arc beam to a series of static step & shoot beams (every 4 degree). The computed dose and effective depth of each static beam were then used to obtain the dose weighted mean effective depth (DWED) for the VMAT beam. The DWED was used for the secondary MU calculation for VMAT plans. Six lung SBRT VMAT plans, eight head and neck VMAT plans and ten prostate VMAT plans that had > 5% MU deviations (failed MU verification) using the AMED method were recalculated with the DWED. For comparison, same number VMAT plans that had < 5% MU deviations (passed MU verification) using AMED method were also reevaluated with the dose weighted mean effective depth method. Results: For MU verification passed plans, the mean and standard deviation of MU differences between the TPS and the secondary calculation program were 2.2%±1.5% for the AMED and 2.1%±1.7% for the DMED method. For the failed plans, the mean and standard deviation of MU differences of TPS to the secondary calculation program were 9.9%±4.7% and 4.7%±2.6, respectively. Conclusion: The dose weighted mean effective depth improved MU calculation accuracy which can be used for the pre-treatment MU verification of VMAT plans.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Magmatic water content controls the pre-eruptive depth of arc magmas

We report that vanguard efforts in forecasting volcanic eruptions are turning to physics-based models, which require quantitative estimates of magma conditions during pre-eruptive storage. Below active arc volcanoes, observed magma storage depths vary widely (~0 to 20 kilometers) and are commonly assumed to represent levels of neutral buoyancy. Here we show that geophysically observed magma depths (6 ± 3 kilometers) are greater than depths of neutral buoyancy, ruling out this commonly assumed control. Observed depths are instead consistent with predicted depths of water degassing. Intrinsically wetter magmas degas water and crystallize deeper than dry magmas, resulting in viscosity increases that lead to deeper stalling of ascending magma. The water–depth relationship provides a critical constraint for forecasting models by connecting depth of eruption initiation to its volatile fuel.

58 GEOSCIENCES↗
DOE OSTI2017

Magnetotelluric Detection Thresholds as a Function of Leakage Plume Depth, TDS and Volume

We conducted a synthetic magnetotelluric (MT) data analysis to establish a set of specific thresholds of plume depth, TDS concentration and volume for detection of brine and CO 2 leakage from legacy wells into shallow aquifers in support of Strategic Monitoring Subtask 4.1 of the US DOE National Risk Assessment Partnership (NRAP Phase II), which is to develop geophysical forward modeling tools. 900 synthetic MT data sets span 9 plume depths, 10 TDS concentrations and 10 plume volumes. The monitoring protocol consisted of 10 MT stations in a 2×5 grid laid out along the flow direction. We model the MT response in the audio frequency range of 1 Hz to 10 kHz with a 50 Ωm baseline resistivity and the maximum depth up to 2000 m. Scatter plots show the MT detection thresholds for a trio of plume depth, TDS concentration and volume. Plumes with a large volume and high TDS located at a shallow depth produce a strong MT signal. We demonstrate that the MT method with surface based sensors can detect a brine and CO 2 plume so long as the plume depth, TDS concentration and volume are above the thresholds. However, it is unlikely to detect a plume at a depth larger than 1000 m with the change of TDS concentration smaller than 10%. Simulated aquifer impact data based on the Kimberlina site provides a more realistic view of the leakage plume distribution than rectangular synthetic plumes in this sensitivity study, and it will be used to estimate MT responses over simulated brine and CO 2 plumes and to evaluate the leakage detectability. Integration of the simulated aquifer impact data and the MT method into the NRAP DREAM tool may provide an optimized MT survey configuration for MT data collection. This study presents a viable approach for sensitivity study of geophysical monitoring methods for leakage detection. The results come in handy for rapid assessment of leakage detectability.

58 GEOSCIENCES↗