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At least 1,207 records · Page 67
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Height Above Nearest Drainage (HAND) and Hydraulic Property Table for CONUS

The continental flood inundation mapping (CFIM) framework is a high-performance computing (HPC)-based computational framework for the Height Above Nearest Drainage (HAND)-based inundation mapping methodology. Using the 10m Digital Elevation Model (DEM) data produced by U.S. Geological Survey (USGS) 3DEP (the 3-D Elevation Program) and the NHDPlus hydrography dataset produced by USGS and the U.S. Environmental Protection Agency (EPA), a hydrological terrain raster called HAND is computed for HUC6 units in the conterminous U.S. (CONUS). The value of each raster cell in HAND is an approximation of the relative elevation between the cell and its nearest water stream. Derived from HAND, a hydraulic property table is established to calculate river geometry properties for each of the 2.7 million river reaches covered by NHDPlus (5.5 million kilometers in total length). This table is a lookup table for water depth given an input stream flow value. Such lookup is available between water depth 0m and 25m at 1-foot interval. The flood inundation map is then computed by using HAND and this lookup table based on the near real-time water forecast from the National Water Model (NWM) at the National Oceanic and Atmospheric Administration (NOAA). HAND and the Hydraulic Property Table version 0.2. is created to correspond to data updates in the USGS 1/3 arcsec DEM, the USGS National Hydrography Dataset, including its Water Boundary Dataset, and the NHDPlus medium resolution dataset. This dataset comprises 331 HUC6 units for CONUS (excluding the five great lakes units), each is a downloadable zip file. Version 0.1 was computed at the National Center for Supercomputing Applications (NCSA) at the University of Illinois at Urban-Champaign in 2016 and is currently hosted at the Texas Advanced Computing Center (TACC). Please cite this data DOI and the following publications and data DOIs when you use HAND and the hydraulic property table: Liu, Yan Y., David R. Maidment, David G. Tarboton, Xing Zheng, and Shaowen Wang. 'A CyberGIS integration and computation framework for high-resolution continental-scale flood inundation mapping.' JAWRA Journal of the American Water Resources Association 54, no. 4 (2018): 770-784. DOI: 10.1111/1752-1688.12660 Zheng, Xing, David G. Tarboton, David R. Maidment, Yan Y. Liu, and Paola Passalacqua. 'River channel geometry and rating curve estimation using height above the nearest drainage.' JAWRA Journal of the American Water Resources Association 54, no. 4 (2018): 785-806. DOI: 10.1111/1752-1688.12661 Liu, Y. (2018). Height Above Nearest Drainage (HAND) for CONUS - v0.1, HydroShare, https://doi.org/10.4211/hs.69f7d237675c4c73938481904358c789 LICENSE FOR USE -- MAPS AND DATA DISCLAIMER This resource is shared under the Creative Commons Attribution CC BY, http://creativecommons.org/licenses/by/4.0/ MAPS AND DATA DISCLAIMER The Oak Ridge National Laboratory (ORNL) shall not be held liable for improper or incorrect use of the data described or information contained on this map or associated series of maps. The data and related map graphics are not legal, land survey or engineering documents and are not intended to be used as such. ORNL gives no warranty, express or implied, as to the accuracy, reliability, utility or completeness of this information. The user of these maps and data assumes all responsibility and risk for the use of the maps and data. ORNL disclaims all warranties, representations or endorsements either express or implied, with regard to the information contained in this map product, including, but not limited to, all implied warranties of merchantability, fitness for a particular purpose or non-infringement. This preliminary map product is for research and review purposes only. It is not intended to be used for emergency management operational or life safety decisions at the local or regional governmental level or by the general public. Users requiring information regarding hazardous conditions or meteorological conditions for specific geographic areas should consult directly with their city or county emergency management office.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Height Above Nearest Drainage (HAND) and Hydraulic Property Table for CONUS - Version 0.21 (20200601)

The continental flood inundation mapping (CFIM) framework is a high-performance computing (HPC)-based computational framework for the Height Above Nearest Drainage (HAND)-based inundation mapping methodology. Using the 10m Digital Elevation Model (DEM) data produced by U.S. Geological Survey (USGS) 3DEP (the 3-D Elevation Program) and the NHDPlus hydrography dataset produced by USGS and the U.S. Environmental Protection Agency (EPA), a hydrological terrain raster called HAND is computed for HUC6 units in the conterminous U.S. (CONUS). The value of each raster cell in HAND is an approximation of the relative elevation between the cell and its nearest water stream. Derived from HAND, a hydraulic property table is established to calculate river geometry properties for each of the 2.7 million river reaches covered by NHDPlus (5.5 million kilometers in total length). This table is a lookup table for water depth given an input stream flow value. Such lookup is available between water depth 0m and 25m at 1-foot interval. The flood inundation map is then computed by using HAND and this lookup table based on the near real-time water forecast from the National Water Model (NWM) at the National Oceanic and Atmospheric Administration (NOAA). HAND and the Hydraulic Property Table version v0.21 is an upgrade to v0.2. Version 0.21 applies NHD HR etching to DEMs for all HUC6 units before running the HAND workflow. This new version also fixed the issue of void areas along HUC boundary. This dataset comprises 331 HUC6 units for CONUS (excluding the five great lakes units), each is a downloadable zip file. Version 0.1 was computed at the National Center for Supercomputing Applications (NCSA) at the University of Illinois at Urban-Champaign in 2016 and is currently hosted at the Texas Advanced Computing Center (TACC). Please cite this data DOI and the following publications and data DOIs when you use HAND and the hydraulic property table: Liu, Yan Y. Height Above Nearest Drainage (HAND) and Hydraulic Property Table for CONUS - Version 0.2. (20200301). Oak Ridge Leadership Computing Facility. (2020). DOI: 10.13139/ORNLNCCS/1608331 Liu, Yan Y., David R. Maidment, David G. Tarboton, Xing Zheng, and Shaowen Wang. 'A CyberGIS integration and computation framework for high-resolution continental-scale flood inundation mapping.' JAWRA Journal of the American Water Resources Association 54, no. 4 (2018): 770-784. DOI: 10.1111/1752-1688.12660 Zheng, Xing, David G. Tarboton, David R. Maidment, Yan Y. Liu, and Paola Passalacqua. 'River channel geometry and rating curve estimation using height above the nearest drainage.' JAWRA Journal of the American Water Resources Association 54, no. 4 (2018): 785-806. DOI: 10.1111/1752-1688.12661 Liu, Y. (2018). Height Above Nearest Drainage (HAND) for CONUS, HydroShare, https://doi.org/10.4211/hs.69f7d237675c4c73938481904358c789 LICENSE FOR USE -- MAPS AND DATA DISCLAIMER This resource is shared under the Creative Commons Attribution CC BY, http://creativecommons.org/licenses/by/4.0/ MAPS AND DATA DISCLAIMER The Oak Ridge National Laboratory (ORNL) shall not be held liable for improper or incorrect use of the data described or information contained on this map or associated series of maps. The data and related map graphics are not legal, land survey or engineering documents and are not intended to be used as such. ORNL gives no warranty, express or implied, as to the accuracy, reliability, utility or completeness of this information. The user of these maps and data assumes all responsibility and risk for the use of the maps and data. ORNL disclaims all warranties, representations or endorsements either express or implied, with regard to the information contained in this map product, including, but not limited to, all implied warranties of merchantability, fitness for a particular purpose or non-infringement. This preliminary map product is for research and review purposes only. It is not intended to be used for emergency management operational or life safety decisions at the local or regional governmental level or by the general public. Users requiring information regarding hazardous conditions or meteorological conditions for specific geographic areas should consult directly with their city or county emergency management office.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Road Lidar Dataset for the TxDOT Austin District

This is a road lidar data collection for developing road elevation models and road inundation mapping methodologies, a joint work between ORNL and The University of Texas at Austin. This dataset is generated as part of the flood transportation infrastructure, partly funded by the NOAA CIROH project. ORNL is a project partner for high-performance computing-empowered flood inundation mapping methodology R&D. The dataset is computed using a GPU-accelerated lidar data processing workflow developed at ORNL. The lidar data source is from TxGIO, the state lidar data collection site. The output dataset is in two formats: laz and copc. It is organized by TxDOT's maintenance sections, covering the Austin District. Data size: 3.86 billion road lidar points, 1.67% of the entire lidar data input Projection: EPSG:32614 (WGS84/UTM zone 14N) Website: https://web.corral.tacc.utexas.edu/nfiedata/road3d/austin_district/AustinMaintenanceSections_H_epsg6343_V_epsg5703/ LICENSE FOR USE -- MAPS AND DATA DISCLAIMER This resource is shared under the Creative Commons Attribution CC BY, http://creativecommons.org/licenses/by/4.0/ MAPS AND DATA DISCLAIMER The Oak Ridge National Laboratory (ORNL) shall not be held liable for improper or incorrect use of the data described or information contained on this map or associated series of maps. The data and related map graphics are not legal, land survey or engineering documents and are not intended to be used as such. ORNL gives no warranty, express or implied, as to the accuracy, reliability, utility or completeness of this information. The user of these maps and data assumes all responsibility and risk for the use of the maps and data. ORNL disclaims all warranties, representations or endorsements either express or implied, with regard to the information contained in this map product, including, but not limited to, all implied warranties of merchantability, fitness for a particular purpose or non-infringement. This preliminary map product is for research and review purposes only. It is not intended to be used for emergency management operational or life safety decisions at the local or regional governmental level or by the general public. Users requiring information regarding hazardous conditions or meteorological conditions for specific geographic areas should consult directly with their city or county emergency management office.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Challenges of COVID-19 Case Forecasting in the US, 2020–2021

During the COVID-19 pandemic, forecasting COVID-19 trends to support planning and response was a priority for scientists and decision makers alike. In the United States, COVID-19 forecasting was coordinated by a large group of universities, companies, and government entities led by the Centers for Disease Control and Prevention and the US COVID-19 Forecast Hub ( https://covid19forecasthub.org ). We evaluated approximately 9.7 million forecasts of weekly state-level COVID-19 cases for predictions 1–4 weeks into the future submitted by 24 teams from August 2020 to December 2021. We assessed coverage of central prediction intervals and weighted interval scores (WIS), adjusting for missing forecasts relative to a baseline forecast, and used a Gaussian generalized estimating equation (GEE) model to evaluate differences in skill across epidemic phases that were defined by the effective reproduction number. Overall, we found high variation in skill across individual models, with ensemble-based forecasts outperforming other approaches. Forecast skill relative to the baseline was generally higher for larger jurisdictions (e.g., states compared to counties). Over time, forecasts generally performed worst in periods of rapid changes in reported cases (either in increasing or decreasing epidemic phases) with 95% prediction interval coverage dropping below 50% during the growth phases of the winter 2020, Delta, and Omicron waves. Ideally, case forecasts could serve as a leading indicator of changes in transmission dynamics. However, while most COVID-19 case forecasts outperformed a naïve baseline model, even the most accurate case forecasts were unreliable in key phases. Further research could improve forecasts of leading indicators, like COVID-19 cases, by leveraging additional real-time data, addressing performance across phases, improving the characterization of forecast confidence, and ensuring that forecasts were coherent across spatial scales. In the meantime, it is critical for forecast users to appreciate current limitations and use a broad set of indicators to inform pandemic-related decision making.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A proposed framework for the development and qualitative evaluation of West Nile virus models and their application to local public health decision-making

West Nile virus (WNV) is a globally distributed mosquito-borne virus of great public health concern. The number of WNV human cases and mosquito infection patterns vary in space and time. Many statistical models have been developed to understand and predict WNV geographic and temporal dynamics. However, these modeling efforts have been disjointed with little model comparison and inconsistent validation. In this paper, we describe a framework to unify and standardize WNV modeling efforts nationwide. WNV risk, detection, or warning models for this review were solicited from active research groups working in different regions of the United States. A total of 13 models were selected and described. The spatial and temporal scales of each model were compared to guide the timing and the locations for mosquito and virus surveillance, to support mosquito vector control decisions, and to assist in conducting public health outreach campaigns at multiple scales of decision-making. Our overarching goal is to bridge the existing gap between model development, which is usually conducted as an academic exercise, and practical model applications, which occur at state, tribal, local, or territorial public health and mosquito control agency levels. The proposed model assessment and comparison framework helps clarify the value of individual models for decision-making and identifies the appropriate temporal and spatial scope of each model. This qualitative evaluation clearly identifies gaps in linking models to applied decisions and sets the stage for a quantitative comparison of models. Specifically, whereas many coarse-grained models (county resolution or greater) have been developed, the greatest need is for fine-grained, short-term planning models (m–km, days–weeks) that remain scarce. We further recommend quantifying the value of information for each decision to identify decisions that would benefit most from model input.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Detecting outbreaks using a spatial latent field

In this paper, we present a method for estimating the infection-rate of a disease as a spatial-temporal field. Our data comprises time-series case-counts of symptomatic patients in various areal units of a region. We extend an epidemiological model, originally designed for a single areal unit, to accommodate multiple units. The field estimation is framed within a Bayesian context, utilizing a parameterized Gaussian random field as a spatial prior. We apply an adaptive Markov chain Monte Carlo method to sample the posterior distribution of the model parameters condition on COVID-19 case-count data from three adjacent counties in New Mexico, USA. Our results suggest that the correlation between epidemiological dynamics in neighboring regions helps regularize estimations in areas with high variance (i.e., poor quality) data. Using the calibrated epidemic model, we forecast the infection-rate over each areal unit and develop a simple anomaly detector to signal new epidemic waves. Our findings show that anomaly detector based on estimated infection-rates outperforms a conventional algorithm that relies solely on case-counts.

Safta, Cosmin [Sandia National Laboratories (SNL-C↗
DOE OSTI2023

End Use Load Profile (EULP) of US Building Sector resulting from mass GHP retrofits

This collection of datasets describes the change (difference) in hourly energy consumption of the U.S. residential and commercial building stock while replacing existing HVAC systems with ground source heat pumps for all the balancing areas in the US, for all buildings eligible for ground source heat pump replacement. Additional county-level data may be requested from the DOE Project Lead.

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

Audi e-tron Green Light Optimized Speed Advisory On-Road Data

To aid researchers in studying the capabilities and benefits of vehicle-to-infrastructure communication, Argonne National Laboratory collected a robust set of on-road driving data of the Audi Green Light Optimized Speed Advisory (GLOSA) system implemented in the e-tron battery electric vehicle. This dataset includes 33 tests, each roughly 27 miles in length and roughly 45 to 75 minutes in duration. The team selected Kane County Highway Route 34 from Main Street in Batavia, Illinois to Middlecreek Lane in St. Charles, Illinois as the route do to its high density of GLOSA-active lights and the most opportunities to observe the system per hour of test time. The data include parameters from the following sources: GLOSA system driving the dash indicators, multiple powertrain parameters including real-time battery power/energy consumption, GPS, front radar gap, and rear radar gap. ![audio-e-tron image](audi-e-tron.jpg)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

2011 Atlanta, Georgia, Regional Travel Survey

The 2011 Regional Travel Survey collected trip data from households across 20 counties in and around Atlanta, Georgia, to improve regional travel demand forecasts. The survey was conducted by PTV NuStats, GeoStats, and PG Americas Inc. on behalf of the Atlanta Regional Commission. The goal was to collect trip data from a minimum of 10,000 households with a subsample of 1,000 households providing global positioning system (GPS) data. The GPS add-on consisted of two sample groups. The first sample group was provided with GPS devices to install in their personal vehicles (797 vehicles) to collect the speed and location of a vehicle during an assigned study period. The second sample group (1,653 participants) was provided with a wearable GPS device to collect the speed and location of an individual during an assigned study period. People participating in the wearable add-on part of the study were chosen because their travel diaries reported use of public transit in day-to-day travel. Each GPS participant was grouped into either the spring phase (March to May 2011) or the fall phase (July to September 2011), and data were recorded for a maximum of seven days for vehicle units and three days for wearable units.

1Hz data↗
DOE OSTI2026

2007 Chicago Regional Household Travel Inventory

To provide data for developing and refining travel demand forecast models, the 2007 Regional Household Travel Inventory studied the demographic and travel behavior characteristics of residents in the greater Chicago area. It included households in eight counties in Illinois and three in Indiana. The Chicago Metropolitan Agency for Planning conducted the study in coordination with the Illinois Department of Transportation, the Northwestern Indiana Regional Planning Commission, and the Indiana Department of Transportation. Travel and activity information for all household members (regardless of age) was collected during a randomly assigned 24- or 48-hour period. It relied on the willingness of households to provide detailed information about their members and vehicles, and to record all travel and associated activity during the collection period. This study also featured a subsample of households collecting data via in-vehicle and wearable global positioning system devices.

1Hz data↗
DOE OSTI2026

2012-2013 Delaware Valley Household Travel Survey

The 2012-2013 Delaware Valley Household Travel Survey collected data for multiple planning purposes such as the calibration of a new activity-based travel demand model. It features data from households across nine counties in the region, including southern New Jersey and southeastern Pennsylvania. The Delaware Valley Regional Planning Commission (DVRPC) sponsored the survey, which was administered by Abt Srbi Inc. A sampling strategy was designed to recruit households for survey participation that would best represent overall regional travel trends. Households were selected randomly, but with special consideration given to under-represented geographies and transit propensity. On their assigned travel day, households were asked to record all trips made within a 24-hour period. Additionally, select households were chosen to participate in a wearable global positioning system (GPS) technology-based component of the study. A total of 811 participants wore the GPS system.

1Hz data↗
DOE OSTI2026

2014-2015 Puget Sound Regional Travel Study

The 2014-2015 Puget Sound Regional Travel Study collected information about household and individual travel patterns for residents throughout a four-county region in Washington State. Study results were used to update the region's travel and land-use models and to calibrate local traffic and travel models. The study also helped the Puget Sound Regional Council (PSRC) and its regional partners develop plans that accommodate the diverse travel needs and preferences of residents. The Resource Systems Group administered the study on behalf of PSRC. Global positioning system (GPS)-equipped smartphones were used to provide data pertaining to the daily travel of 547 individual participants. Because the region's university students may have been underrepresented in the initial 2014 household travel study, the PSRC added a college-population travel survey in fall 2014. In spring 2015, a second household data collection effort was conducted to increase the frequency of data collection and to collect GPS data as well as a sample of longitudinal data from households that completed the 2014 survey.

1Hz data↗
DOE OSTI2026

2001-2002 Southern California Regional Travel Survey

The 2001-2002 Southern California Regional Travel Survey collected data on household characteristics and travel behavior to update regional travel demand models. It covered six counties including Imperial, Los Angeles, Orange, Riverside, San Bernardino, and Ventura. The Southern California Association of Governments contracted with NuStats Partners to conduct the survey following the 2000 decennial census. Survey data collection occurred in 2001 and 2002 using computer-assisted telephone interviewing and travel diaries. Roughly 17,000 households completed the survey. An additional global positioning system (GPS) sample was taken for the purpose of auditing the self-reported diaries. Battelle provided data collection support for the GPS sample.

1Hz data↗
DOE OSTI2026

2001 Atlanta Household Travel Survey

The 2001 Atlanta Household Travel Survey collected demographic, socioeconomic, and travel information on work and non-work travel behavior for a 48-hour travel period. The study was conducted on behalf of the Atlanta Regional Commission, and it is an essential element in the transportation planning and modeling efforts for the 13-county Atlanta region. The main objective of the study was to produce data that could be used to develop and calibrate travel demand models for use in travel forecasting, land use planning, and air quality planning to improve the transportation system. The second component in the survey was the deployment of an electronic travel diary with person-based GPS and an accelerometer to collect health and activity information. Travel data includes trip generation, trip distribution, and modal choice. The survey recruited a total of 12,184 households to participate in the study. Of these, 8,069 households (66%) completed travel. The 8,069 households, when weighted, represent 21,323 persons, 14,449 vehicles, and 126,127 places visited from April 2001 through April 2002. This dataset is part of the Metropolitan Travel Survey Archive, which includes travel surveys from numerous public agencies across the United States and is archived by the Transportation Secure Data Center to ensure their continued public availability.

1Hz data↗
DOE OSTI2026

2000-2001 California Statewide Household Travel Survey

The 2000-2001 California Statewide Household Travel Survey, conducted under the auspices of the California Department of Transportation, was conducted between October 2000 and December 2001 among households located in each of California's 58 counties. The purpose of the study was to update the statewide database of household socioeconomic and travel information and helped to refine travel estimates, models, and forecasts throughout California. The survey was an essential element in determining statewide and regional travel patterns. A total of 17,040 households participated in the survey, contributing household socioeconomic and travel data. Travel variables collected include trip times, mode, activity at location, origin and destination, and vehicle occupancy, among other travel-related data from 134,173 trips. Residents completed diary records of their daily travel over a 24-hour (weekday) or 48-hour period (Friday/Saturday or Sunday/Monday pair).

1Hz data↗
DOE OSTI2026

2002 Champaign-Urbana Urbanized Area Transportation Study

This travel survey, conducted by Champaign County Regional Planning Commission, collected daily weekday travel data from 395 urbanized-area households—accounting for every individual 12 years old or older—in the Champaign-Urbana-Savoy region. The information gathered from the survey was used to forecast future transportation demand and update the Champaign Urbana Urbanized Area Transportation Study Long Range Transportation Plan 2035. Of the 395 households recruited for the study, 362 completed their travel logs. The household travel survey was completed during the summer and fall of 2002, and it was the first large-scale attempt at collecting single-weekday travel data in Champaign, Urbana, and Savoy.

1Hz data↗
DOE OSTI2026

1994 Cleveland Area Home Interview Travel Survey

The Greater Cleveland Regional Transit Authority and the Northeast Ohio Areawide Coordinating Agency conducted a home-interview travel survey to update information on current travel patterns and trip characteristics in the northeast Ohio area. The survey was administered during the spring of 1994 to 1,651 households, and 17,101 trips were recorded. The survey included 1,056 households from Cuyahoga County. Participants were assigned a travel day (Monday through Friday only) and mailed a household survey form and travel diaries for each household member five years old or older.

1Hz data↗