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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

CHESS 2025: Spectrometer orthorectified at-sensor radiance from NEON AOP imaging spectroscopy surveys

This dataset provides Level 1 (L1) orthorectified at-sensor radiance derived from measurements collected by the Imaging Spectrometer-1 (NIS-1) onboard the NEON (National Ecological Observatory Network) Airborne Observation Platform (AOP) for the 2025 Colorado Headwaters Ecological Spectroscopy Study (CHESS). NIS-1 captures light reflected from the Earth’s surface in 426 discrete wavelength bands as raw digital numbers (DNs; Level 0). These data are then calibrated to physical units (uW/cm²·sr·nm) following the processing steps described in the NEON Imaging Spectrometer Level 1B Calibrated Radiance Algorithm Theoretical Basis Document (ATBD; Gallery 2022). The data delivered here are the primary inputs for the surface reflectance product in “Custom surface reflectance, shade masks, and equivalent water thickness maps for the Colorado Headwaters Ecological Spectroscopy Study” (Carroll et al. 2026). For intertemporal comparison, the radiance data here are most directly relatable to the v2 radiance data in “NEON AOP Imaging Spectroscopy Survey of Upper East River Colorado Watersheds: Raw-Space Radiance and Observational Variable Dataset” (Goulden et al. 2018), to which the same processing methodology was applied. Together, the radiance and reflectance data enable users to exploit the unique reflection signatures of different surface objects for land cover classification, foliar trait mapping, plant vigor assessment, water content estimation, trace-element identification, and other scientific applications. The data were acquired over three study domains in the Upper Gunnison river basin: the upper East River watershed (CRBU); Almont Triangle and Taylor Canyon (ALMO); and Upper Taylor River watershed (UPTA) between 2025-06-13 and 2025-07-15. Within each domain, data are delivered by flightline as orthorectified and calibrated hyperspectral rasters in Hierarchical Data Format version 5 (HDF5) format, with radiance values provided in uW/cm²·sr·nm on a fixed, uniform Universal Transverse Mercator (UTM) grid at 1 meter spatial resolution. The radiance rasters include all 426 NIS-1 spectral bands, along with associated quality-assurance (QA) and diagnostic and ancillary layers needed for atmospheric correction workflows. Orthorectified radiance is produced from pushbroom spectrometer observations by applying NEON’s radiometric calibration (including bad pixel masking, dark subtract, dark pedestal shift correction, electronic panel ghost correction, grating ghost correction, deblur correction and flat-fielding) and spectral calibration (using spectral response function band centers and full-width at half-maximum intensity), followed by geolocation and regridding to the fixed grid. CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgement: Field and remote-sensing data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). This work was also supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2020

Vertical profiles of light absorption and scattering associated with black carbon particle fractions in the springtime Arctic above 79° N

Despite the potential importance of black carbon (BC) for radiative forcing of the Arctic atmosphere, vertically resolved measurements of the particle light scattering coefficient (σ sp ) and light absorption coefficient (σ ap ) in the springtime Arctic atmosphere are infrequent, especially measurements at latitudes at or above 80° N. Here, relationships among vertically distributed aerosol optical properties (σap, σsp and single scattering albedo or SSA), particle microphysics and particle chemistry are examined for a region of the Canadian archipelago between 79.9 and 83.4° N from near the surface to 50 hPa. Airborne data collected during April 2015 are combined with ground-based observations from the observatory at Alert, Nunavut and simulations from the Goddard Earth Observing System (GEOS) model, GEOS-Chem, coupled with the TwO-Moment Aerosol Sectional (TOMAS) model (collectively GEOS-Chem–TOMAS; Kodros et al., 2018) to further our knowledge of the effects of BC on light absorption in the Arctic troposphere. The results are constrained for σsp less than 15 Mm -1 , which represent 98% of the observed σsp, because the single scattering albedo (SSA) has a tendency to be lower at lower σsp, resulting in a larger relative contribution to Arctic warming. At 18.4 m 2 g -1 , the average BC mass absorption coefficient (MAC) from the combined airborne and Alert observations is substantially higher than the two averaged modelled MAC values (13.6 and 9.1 m2 g -1 ) for two different internal mixing assumptions, the latter of which is based on previous observations. The higher observed MAC value may be explained by an underestimation of BC, the presence of small amounts of dust and/or possible differences in BC microphysics and morphologies between the observations and model. In comparing the observations and simulations, we present σap and SSA, as measured, and σ ap 2 and the corresponding SSA to encompass the lower modelled MAC that is more consistent with accepted MAC values. Median values of the measured σap, rBC and the organic component of particles all increase by a factor of 1.8±0.1, going from near-surface to 750 hPa, and values higher than the surface persist to 600 hPa. Modelled BC, organics and σap agree with the near-surface measurements but do not reproduce the higher values observed between 900 and 600 hPa. The differences between modelled and observed optical properties follow the same trend as the differences between the modelled and observed concentrations of the carbonaceous components (black and organic). Model-observation discrepancies may be mostly due to the modelled ejection of biomass burning particles only into the boundary layer at the sources. For the assumption of the observed MAC value, the SSA range between 0.88 and 0.94, which is significantly lower than other recent estimates for the Arctic, in part reflecting the constraint of σsp<15 Mm -1 . The large uncertainties in measuring optical properties and BC, and the large differences between measured and modelled values here and in the literature, argue for improved measurements of BC and light absorption by BC and more vertical profiles of aerosol chemistry, microphysics and other optical properties in the Arctic.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

The Influence of Aqueous Dispersivity on Transport of Radiological Material at LANL MDA G - 20450

Aqueous dispersivity is a phenomenon by which a solute moving in groundwater mixes with water that does not contain the solute and becomes diluted. Solutes take paths of varying lengths through pore networks or experience slower velocities due to interactions with pore walls. Aqueous dispersivity leads to breakthrough curves with earlier arrival than expected in purely advective systems and persistent 'tails,' or late-arriving, dilute concentrations of the solute. Dispersivity can be longitudinal (in the direction of flow) or transverse (orthogonal to flow). LANL has disposed of radioactive material at Material Disposal Area (MDA) G since the late 1950's. Most of the disposed materials at MDA G are low-level waste (LLW), but other types of radioactive wastes are disposed or stored at the site. For example, there is a collection of remote-handled transuranic waste (RH-TRU) waste in 33 shafts, which were intended to be stored retrievably. Aqueous-phase radionuclides included in models of contaminant transport at MDA G are subject to dispersive processes in porous media of the vadose zone and saturated zone. To model dispersivity at MDA G probabilistically, we created site-specific distributions of aqueous dispersivity. First, we performed a comprehensive review of peer-reviewed publications, reports from other performance assessments of MDA G, and documentation from sites that were considered similar based on expert opinion, such as Yucca Mountain. Data were included from a wide range of sources, so that distributions developed based on the data reflect the current state of knowledge in properties of the subsurface environment. Data were grouped based on value type (e.g., determined via experiment or used in a different model), rock type, zone type (saturated or unsaturated), and site, and were weighted in the distributions according to their relevance to the site and zone type. We combined Monte Carlo simulation and bootstrapping techniques to produce approximate distributions of mean aqueous dispersivity. Distributions of dispersivity for saturated and unsaturated zone transport are differentiated by data weight and the length scales over which transport occurs. We find that underestimating or disregarding transverse dispersivity may lead to overstating simulated well capture in the saturated zone at MDA G. Neglecting transverse dispersivity is a common assumption due in part to use of one-dimensional transport models that do not consider solute spreading orthogonal to flow. Also, we find that variability in longitudinal dispersivity is associated with substantial travel time alterations in the MDA G unsaturated zone and in simulated contaminant breakthrough times at a downstream observation plane in the MDA G saturated zone. Finally, at large scales (>1000 m), a linear trend between the dispersivity coefficient and length over which transport occurs may overestimate the dispersivity coefficient. Future modeling work with application to solute spreading could include interacting advective and diffusive zones in the highly fractured basalt below MDA G. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Quantifying optical loss of high-voltage degradation modes in photovoltaic modules using spectral analysis

The direct current bias for photovoltaic (PV) modules interconnected in series-strings may include both high voltage negative (“HV-”) and positive (“HV+”) polarity with respect to the electrical ground. Multiple degradation modes, resulting in quantifiable optical loss, were found to occur during HV-/HV+ sequential stress, including corrosion of the external glass surface, encapsulant delamination (at its interfaces with the glass and the PV cell), internal haze formation (resulting from a chemical interaction between the glass and the encapsulant), corrosion and migration of the gridlines, and corrosion of the silicon nitride (Si x N y ) antireflective coating on the cell. The effects of these separate modes were examined using monolithic (e.g., glass or PV cell) and laminated-coupon (glass/encapsulant/glass or glass/encapsulant/cell/encapsulant/backsheet) specimens. Characterizations during and after unbiased accelerated testing at 85°C/85% relative humidity included spectrophotometry, optical microscopy, electron microscopy, and ellipsometry. For some module components (i.e., the glass and the Si x N y coating), the optical performance was determined through iterative analysis of empirical measurements. Concentrating on just their spectral effect, a novel model was then developed to estimate the transfer of light to the PV cell and the return of light from the PV module with simultaneous degradation mechanisms, which was compared with a mini-module previously subjected to HV-/HV+ stress. Here the model suggests that one third of the current loss observed for the mini-module can be attributed to the optical degradation of the packaging materials. The dominant degradation modes include encapsulant delamination and corrosion of the Si x N y coating. Recommendations are given so that the optical model may be improved relative to accelerated testing and validated relative to field aging.

14 SOLAR ENERGY↗
DOE OSTI2023

Quantifying Optical Loss of High-Voltage Degradation Modes in PV Modules Using Spectral Analysis [Slides]

The direct current bias for photovoltaic (PV) modules interconnected in series-strings may include both high voltage negative ("HV-") and positive ("HV+") polarity with respect to the electrical ground. Multiple degradation modes, resulting in quantifiable optical loss, were found to occur during HV-/HV+ sequential stress, including: corrosion of the external glass surface, encapsulant delamination (at its interfaces with the glass and the PV cell), internal haze formation (resulting from a chemical interaction between the glass and the encapsulant), corrosion and migration of the gridlines, and corrosion of the silicon nitride (SixNy) antireflective coating on the cell. The effects of these separate modes were examined using monolithic (e.g., glass or PV cell) and laminated-coupon (glass/encapsulant/glass or glass/encapsulant/cell/encapsulant/backsheet) specimens. Characterizations during and after unbiased accelerated testing at 85 degrees C/85% relative humidity included: spectrophotometry, optical microscopy, electron microscopy, and ellipsometry. For some module components (i.e., the glass and the SixNy coating), the optical performance was determined through iterative analysis of empirical measurements. Concentrating on just their spectral effect, a novel model was then developed to estimate the transfer of light to the PV cell and the return of light from the PV module with simultaneous degradation mechanisms, which was compared to a mini-module previously subjected to HV-/HV+ stress. The model suggests one-third of the current loss observed for the mini-module can be attributed to the optical degradation of the packaging materials. The dominant degradation modes include encapsulant delamination and corrosion of the SixNy coating. Recommendations are given so that the optical model may be improved relative to accelerated testing and validated relative to field aging.

14 SOLAR ENERGY↗
DOE OSTI2025

IM3 Projected US Data Center Locations

IM3 Projected US Data Center Locations This dataset contains model projections of new data center facilities in the contiguous United States (CONUS) through 2035 using the CERF – Data Centers model. Data center locations are modeled across four data center electricity demand growth scenarios (low, moderate, high, higher) and five market gravity scenarios (0%, 25%, 50%, 75%, 100%). Projected locations are intended to be regional representations of feasible siting locations in the future to assess potential grid and water stress impacts. The data center load growth scenarios correspond with the rates outlined in EPRI (2024) and include 3.71%, 5%, 10%, and 15% annual growth of electricity demand for data centers from 2023 values in 37 states across the CONUS. Market gravity scenarios correspond to the relative importance of proximity to data center markets or high population areas compared to locational cost in the siting algorithm. 0% market gravity means that siting decisions were entirely determined by the locational cost in each feasible location. 100% market gravity means that only market proximity was considered when siting. Other scenarios have weight placed on both components where total weight always equals 100%. Locational cost is dependent on facility cooling type and corresponding electricity cost, taxes, and other factors. Facility cooling type is spatially determined where high water stress and/or areas with high summer wet bulb temperatures are assumed to operate with mechanical cooling for a higher fraction of the year rather than evaporative cooling. Feasible data center siting areas are based on geospatial suitability raster data developed with open-source information. The following areas are excluded from siting: Areas within 300 m of a federal airport runway Waterbodies Areas with slope >16% Areas susceptible to sinkholes High coastal or inland flood risk areas Local, state, and federal parks, leisure areas, and cemeteries Areas >2 km away from electric substations Areas >5 km away from a municipal water supplier service area Areas >2 km away from high-speed fiber provider service territory Protected Areas Database of the United States (PAD-US) areas Railroads, major roadways, and minor roadways Military areas and training grounds NLCD developed lands Areas >0.8 km (0.5 miles) from NLCD developed lands Because we use open-source information, proprietary information that can influence siting decisions such as individual tax agreements with cities, detailed fiber line connectivity, electric grid power capacity agreements, and others, are not currently accounted for in the modeling process. Using specific building locations and footprints in the dataset for local planning purposes is not advised. Technical Information Geospatial data is provided in geojson format using the Albers Equal Area Conic (ESRI:102003) coordinate reference system. The datasets contain the following parameters: id - unique identification number within given scenario file growth_scenario – data center demand growth scenario market_gravity_weight – market gravity weight scenario (%) region – name of region (i.e., US State) total_cost_million_usd – locational siting cost ($million) campus_size_square_ft – total land acquired for data center facility (square ft) data_center_it_power_mw – IT power of data center facility (MW) mechanical_cooling_frac – fraction of year when data center uses mechanical cooling system water_cooling_frac– fraction of year when data center uses evaporative cooling system cooling_energy_demand_mwh – total annual facility energy demand for cooling (MWh) cooling_water_demand_mgy – total annual facility water demand for cooling (MG) cooling_water_consumption_mgy – total annual facility water consumed (MG) normalized_locational_cost – normalized total locational cost score for location normalized_gravity_score – normalized market gravity score for location weighted_siting_score – total weighted siting score of locational cost and gravity score geometry – polygon geometry of facility Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License This data is made available under a CCBY4.0 License Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830

Mongird, Kendall (ORCID:0000000328077088)↗
DOE OSTI2026

IM3 Projected US Data Center Locations

IM3 Projected US Data Center Locations This dataset contains model projections of new data center facilities in the contiguous United States (CONUS) through 2035 using the CERF – Data Centers model. Data center locations are modeled across four data center electricity demand growth scenarios (low, moderate, high, higher) and five market gravity scenarios (0%, 25%, 50%, 75%, 100%). Projected locations are intended to be regional representations of feasible siting locations in the future to assess potential grid and water stress impacts. The data center load growth scenarios correspond with the rates outlined in EPRI (2024) and include 3.71%, 5%, 10%, and 15% annual growth of electricity demand for data centers from 2023 values in 37 states across the CONUS. Market gravity scenarios correspond to the relative importance of proximity to data center markets or high population areas compared to locational cost in the siting algorithm. 0% market gravity means that siting decisions were entirely determined by the locational cost in each feasible location. 100% market gravity means that only market proximity was considered when siting. Other scenarios have weight placed on both components where total weight always equals 100%. Locational cost is dependent on facility cooling type and corresponding electricity cost, taxes, and other factors. Facility cooling type is spatially determined where high water stress and/or areas with high summer wet bulb temperatures are assumed to operate with mechanical cooling for a higher fraction of the year rather than evaporative cooling. Feasible data center siting areas are based on geospatial suitability raster data developed with open-source information. The following areas are excluded from siting: Areas within 300 m of a federal airport runway or within an airport area boundary Waterbodies Areas with slope >16% Areas susceptible to sinkholes High coastal or inland flood risk areas Local, state, and federal parks, leisure areas, and cemeteries Areas >2 km away from electric substations Areas >5 km away from a municipal water supplier service area Areas >2 km away from high-speed fiber provider service territory USGS Protected Areas Database of the United States (PAD-US) GAP status 1, 2, or 3 areas US National Parks Wetlands USFWS critical habitats BIA land areas Railroads, major roadways, and minor roadways Military areas and training grounds NLCD developed lands Areas >0.8 km (0.5 miles) from NLCD developed lands Because we use open-source information, proprietary information that can influence siting decisions such as individual tax agreements with cities, detailed fiber line connectivity, electric grid power capacity agreements, and others, are not currently accounted for in the modeling process. Using specific building locations and footprints in the dataset for local planning purposes is not advised. Technical Information Geospatial data is provided in geojson format using the Albers Equal Area Conic (ESRI:102003) coordinate reference system. The datasets contain the following parameters: id - unique identification number within given scenario file growth_scenario – data center demand growth scenario market_gravity_weight – market gravity weight scenario (%) region – name of region (i.e., US State) total_cost_million_usd – locational siting cost ($million) campus_size_square_ft – total land acquired for data center facility (square ft) data_center_it_power_mw – IT power of data center facility (MW) mechanical_cooling_frac – fraction of year when data center uses mechanical cooling system water_cooling_frac– fraction of year when data center uses evaporative cooling system cooling_energy_demand_mwh – total annual facility energy demand for cooling (MWh) cooling_water_demand_mgy – total annual facility water demand for cooling (MG) cooling_water_consumption_mgy – total annual facility water consumed (MG) normalized_locational_cost – normalized total locational cost score for location normalized_gravity_score – normalized market gravity score for location weighted_siting_score – total weighted siting score of locational cost and gravity score geometry – polygon geometry of facility Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License This data is made available under a CCBY4.0 License Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830

Mongird, Kendall (ORCID:0000000328077088)↗
DOE OSTI2025

Package Data for CERF-Data Centers

This dataset contains sample input 100m resolution raster files for running the CERF-DC python package (see https://github.com/IMMM-SFA/cerf_data_centers) at the state level across the CONUS. Due to data availability constraints, some of the items included in this dataset are proxies or assumptions for siting factors used in the model. These are individually noted in the item descriptions and can be exchanged with more detailed information upon availability. Data Descriptions The following raster files are included in the data download: state_siting_region.tif — State areas identified by state FIPS code composite_siting_suitability.tif — Value of 1 indicates suitable siting location, 0 otherwise. The following areas are excluded from siting: Areas within 300m of a federal airport runway Waterbodies Areas with slope >16% Areas susceptible to sinkholes High coastal or inland flood risk areas Local, state, and federal parks, leisure areas, and cemeteries Areas >2 km away from electric substations Areas >5 km away from a municipal water supplier service area Areas >2 km away from high-speed fiber provider service territory Protected Areas Database of the United States (PAD-US) areas Railroads, major roadways, and minor roadways Military areas and training grounds Developed lands Areas >0.8 km (0.5 miles) from developed lands land_value_dollar_per_sqft.tif — USD per square foot (sqft) derived from USDA $/acre land cost personal_property_tax_rate.tif — Personal property tax rate by state. Uses an assumed 0.0125 personal property tax rate for states with personal property tax, 0 for states without personal property tax. real_property_tax_rate.tif — Real property tax rate. Based on county level residential real estate property tax rates. sales_tax_rate.tif — Sales tax rate by state. mechanical_cooling_fraction.tif — Fraction of year (values between 0 and 1, inclusive) that the data center would be cooled through mechanical processes based on local water stress and humidity levels. water_cooling_fraction.tif — Fraction of year (values between 0 and 1, inclusive) that the data center would be cooled through evaporative (water cooled) processes based on local water stress and humidity levels. distance_to_substation.tif — Distance to nearest substation in hundreds of meters (i.e., value of 1 equals a distance of 100m). Offshore areas have a value of 0. industrial_electricity_rates_dollar_per_kwh.tif — USD/kWh industrial electricity rates. Represents the average industrial rate across all utilities that operate within a given county. Values are derived from the US Utility Rate Database. commercial_electricity_rates_dollar_per_kwh.tif — USD/kWh commercial electricity rates. Represents the average commercial rate across all utilities that operate within a given county. Values are derived from the US Utility Rate Database. data_center_market_locations.tif — Grid cells with positive values represent the centroid of existing data center market clusters. The value of non-zero grid cells represents the number of data centers in the market cluster. All other grid cells have a value of 0. Geospatial Metadata CRS: Albers Equal Area Conic (ESRI:102003) Extent: -2415585.0000000023283064,-1441981.2605773280374706 : 2384414.9999999976716936,1708018.7394226719625294 Dimensions: X: 48000 Y: 31500 Bands: 1 Origin: -2415585.0000000023283064,1708018.7394226719625294 Pixel Size: 100,-100 Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License This data is made available under a CCBY4.0 License Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830

Mongird, Kendall↗
DOE OSTI2026

Package Data for CERF-Data Centers

This dataset contains sample input 100m resolution raster files for running the CERF-DC python package (see https://github.com/IMMM-SFA/cerf_data_centers) at the state level across the CONUS. Due to data availability constraints, some of the items included in this dataset are proxies or assumptions for siting factors used in the model. These are individually noted in the item descriptions and can be exchanged with more detailed information upon availability. Data Descriptions The following raster files are included in the data download: state_siting_region.tif — State areas identified by state FIPS code composite_siting_suitability.tif — Value of 1 indicates suitable siting location, 0 otherwise. The following areas are excluded from siting: Areas within 300 m of a federal airport runway or within an airport area boundary Waterbodies Areas with slope >16% Areas susceptible to sinkholes High coastal or inland flood risk areas Local, state, and federal parks, leisure areas, and cemeteries Areas >2 km away from electric substations Areas >5 km away from a municipal water supplier service area Areas >2 km away from high-speed fiber provider service territory USGS Protected Areas Database of the United States (PAD-US) GAP status 1, 2, or 3 areas US National Parks Wetlands USFWS critical habitats BIA land areas Railroads, major roadways, and minor roadways Military areas and training grounds NLCD developed lands Areas >0.8 km (0.5 miles) from NLCD developed lands land_value_dollar_per_sqft.tif — USD per square foot (sqft) derived from USDA $/acre land cost personal_property_tax_rate.tif — Personal property tax rate by state. Uses an assumed 0.0125 personal property tax rate for states with personal property tax, 0 for states without personal property tax. real_property_tax_rate.tif — Real property tax rate. Based on county level residential real estate property tax rates. sales_tax_rate.tif — Sales tax rate by state. mechanical_cooling_fraction.tif — Fraction of year (values between 0 and 1, inclusive) that the data center would be cooled through mechanical processes based on local water stress and humidity levels. water_cooling_fraction.tif — Fraction of year (values between 0 and 1, inclusive) that the data center would be cooled through evaporative (water cooled) processes based on local water stress and humidity levels. distance_to_substation.tif — Distance to nearest substation in hundreds of meters (i.e., value of 1 equals a distance of 100m). Offshore areas have a value of 0. industrial_electricity_rates_dollar_per_kwh.tif — USD/kWh industrial electricity rates. Represents the average industrial rate across all utilities that operate within a given county. Values are derived from the US Utility Rate Database. commercial_electricity_rates_dollar_per_kwh.tif — USD/kWh commercial electricity rates. Represents the average commercial rate across all utilities that operate within a given county. Values are derived from the US Utility Rate Database. data_center_market_locations.tif — Grid cells with positive values represent the centroid of existing data center market clusters. The value of non-zero grid cells represents the number of data centers in the market cluster. All other grid cells have a value of 0. Geospatial Metadata CRS: Albers Equal Area Conic (ESRI:102003) Extent: -2415585.0000000023283064,-1441981.2605773280374706 : 2384414.9999999976716936,1708018.7394226719625294 Dimensions: X: 48000 Y: 31500 Bands: 1 Origin: -2415585.0000000023283064,1708018.7394226719625294 Pixel Size: 100,-100 Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License This data is made available under a CCBY4.0 License Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830

Mongird, Kendall↗
DOE OSTI2025

Experimental and fuel-surrogates modeling study of the high-pressure pyrolysis of specialty cetane number fuels: implications for fall-off in ethylene unimolecular dissociation

Single pulse shock tube experiments were conducted at 50 atm nominal pressure and 4 ms nominal reaction time over a temperature range of 900–1800 K, to study the pyrolysis speciation of a multi-component jet fuel, F-24, and six cetane number (CN) specialty fuels - CN30, CN35, CN40, CN45, CN50, and CN55. Gas chromatography (GC) was used to qualitatively and quantitatively analyze the post shock gases. The relationship between the formation of key pyrolysis species and the chemically controlled combustion propensity as reflected by the cetane number of each fuel was examined. A surrogate-based mechanism from the CRECK Modelling Group and chemical-functional group based optimized surrogates (CFGO) were used to simulate the pyrolysis speciation results. The model was able to capture the chemistry of most species except two important pyrolysis intermediates – ethylene and acetylene. Chemical kinetic analyses were performed to identify the important reactions which affect the chemistry of these species; however, the rate parameters of critical reactions were found to be unsuitable for simulating the present high-pressure studies. Here, to address this unsuitability, a theory-based fall-off analysis for three reactions representing the decomposition of ethylene and subsequent formation of acetylene was performed, and these are included in an updated version of the CRECK mechanism. This update resolves discrepancies between the experimental results and simulations for ethylene and acetylene. Reaction flux analyses using the updated surrogate model were also performed to identify the important reaction pathways responsible for the formation of crucial species and to provide an analysis of the chemistry of complex multi-component fuel systems. The fundamental reactions responsible for driving pyrolysis chemistry were greatly influenced by the chemical functional groups present in these fuels. In addition to updating the rate parameters of specific reactions to improve modeling, this study also emphasizes the effectiveness of the fuel-surrogate approach, where surrogates representing the chemical functional group composition of the parent fuel serve as a valuable tool for predicting the combustion chemistry of novel fuels.

Chemical Kinetics↗
DOE OSTI2020

Bayesian Approach to Estimation of Water Table Elevations Using Historical Rasters as Prior Information 2019 - 20430

In cases of complex but only partially known geology and a lack of spatial control in observation well locations, water table elevation estimation is very challenging. In some cases, auxiliary information, such as observations of the movement of tracers, operation of injection and extraction wells, and calibration of groundwater models against the historical elevation data, can be combined with expert judgement to estimate flow directions in areas of sparse data and to aid in the production of more reliable contour maps (and associated rasters) than could be produced by relying on sparse well elevation data alone. Given a historical sequence of these raster maps, the question arises how to automate, to the extent possible, the process of producing new raster maps to reflect data from previous times, the current data and the operation of expert judgement. One solution is to adopt a Bayesian point of view and to regard the historical well elevation data, auxiliary information and historical raster maps as prior information. The well elevations for water table wells, as well as those for injection/extraction wells and the data associated with other relevant variables, can be viewed as predictors for the raster surface. From this prior information, we can, conditional on the values of these predictors for a new time period, compute an expected value map and a standard deviation map for the new raster. These then can be taken to specify a prior predictive distribution for the pixels in the new raster map. Then we condition the pixels, corresponding to water level observation wells within the raster, on the observed values in those wells (which in general will differ from the regression estimate) for the new time period. Given the smoothness of the water table surface, we then smooth the surface of deviations from the mean surface, based on the variograms of the historical rasters, and add this smoothed surface to the regression mean surface. The error structure of the produced raster map is defined by the regression error structure and the error due to smoothing based on the estimated variograms. This methodology has been developed and is being further refined for groundwater monitoring and remediation at LANL. It is a very flexible method that can also be applied with a variety of other predictors applied to model the water level wells in the area of interest over the historical record. The smoothness of the spatial process and its possible evolution over time can then be estimated from the residuals from this regression. This can be augmented by expert hydrogeological opinion based on site topography and hydrogeology. (authors)

54 ENVIRONMENTAL SCIENCES↗