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DOE OSTI2017

Dry Storage Cask Inventory Assessment

The report, Commercial Spent Nuclear Fuel and High-Level Radioactive Waste Inventory Report [FCRDNFST-2013-000263, Vinson/Metzger/Carter 2017], provides information on the inventory of commercial spent nuclear fuel (SNF) as well as Government-owned SNF and High Level Waste (HLW) in the U.S. Department of Energy (DOE) complex. Inventory forecasts for commercial SNF were made for a few selected scenarios of future commercial nuclear power generation involving the existing reactor fleet including one scenario involving reactors under construction. This Dry Storage Cask Inventory Assessment report uses the data contained in FCRD-NFST-2013- 000263 to define the existing inventory of SNF in dry storage. This information is integrated with data on dry storage canisters and casks from the report, Storage and Transport Cask Data for Used Commercial Nuclear Fuel, 2013 U. S. Edition [ATI-TR-13047 2013] in a Microsoft Access database (hereinafter referred to as the Dry Storage Cask/Inventory Database). The Dry Storage Cask/Inventory Database is used to produce queries for assessing the various systems used for the existing inventory of dry storage casks. The database and this report will be revised in the future as the inventory of fuel in dry storage changes and as additional information becomes available. Both bare fuel and canistered dry storage casks are currently used to dry store SNF. Only 4 utilities currently use bare fuel storage systems for dry storage at 5 reactor sites. Seven unique bare fuel storage cask systems are used to store a total of 9,694 spent fuel assemblies in 212 total casks.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2017

First Look Fund - An Early-Stage Aligned Intermediary to Enhance the Flow of Private Capital into Energy Companies (Final Technical Report)

The technologies required to advance society into a cleaner, better future will come from a broad range of scientific disciplines including materials science, chemistry, physics, and biology. The innovations will rely on scientific breakthroughs rather than software innovation and can be referred to as “hardtech.” These new technology developments will have vast implications across most industrial sectors: energy, materials, manufacturing, electronics, life sciences, etc. The majority of these scientific breakthroughs will be produced and developed by startup companies, which are best suited for high-risk, early-stage development work. However, a large funding gap exists between lab-scale scientific research of the startup world and industrial-scale commercialization. Activate Global, Inc (Activate), a 501(c)3 nonprofit, fills one part of this gap with the Activate Fellowship program1.To fully bridge the gap between lab and market, additional and aligned capital sources are needed. Through this project, Activate’s goals were: Research the recent history (2005-2015) of hardtech investing to discover the driving forces behind positive and negative outcomes from the viewpoint of investors.; Create a database of “technology pathways” categorized by specific technology areas and showing what technology milestones would be needed to be attractive to a partner or acquirer and what resources were available to achieve those goals.; Build a financial model supported by the data acquired that would show attractive returns were possible in early-stage hardtech investing.; Create an investment thesis around the financial model to show how attractive returns could be achieved in an early-stage hardtech-focused fund.; Publish a paper with a qualitative comparative analysis to help investors better understand driving factors of outcomes.; Share these findings with asset managers not currently investing in early-stage hardtech to attract partners to execute the investment strategy.; Implement the investment strategy and begin investing in early-stage hardtech companies through a new vehicle: First Look Fund. Through this research, Activate created a database of nearly 200 hardtech companies that exited via an M&A transaction between 2005 and 2015. Much of the data Activate obtained was nonpublic information and therefore, the database was anonymized. Activate also created the technology pathways database to help new early-stage hardtech companies define milestones, technology objectives and potential resources. Activate created a financial model with a legal structure showing that market-rate returns were possible, created an investment thesis around that model, and then shared these findings with potential partners. Finally, Activate’s research was used to publish a paper with Stanford on driving factors behind financial outcomes that will help investors better guide their portfolios. While Activate was not able to launch the First Look Fund during the project timeline, mostly due to the fallout from COVID-19 (global economic shocks decreasing interest in first time funds, inability to meet potential investors in person), the research has created enough interest for Activate to continue pursuing the goals of the project, showing the potential feasibility of the endeavor. Regardless of Activate’s ability to execute on the investment strategy, the resulting research has been downloaded, viewed, and referenced by investors and becomes another tool to encourage investment in the early-stage hardtech space.

14 SOLAR ENERGY↗
DOE OSTI2020

Generic FEPs Catalogue and Salt Knowledge Archive

This report describes the development of a comprehensive catalogue of generic features, events, and processes (FEPs) that are potentially important for the post-closure performance of a repository for high-level radioactive waste (HLW) and spent nuclear fuel (SNF) in salt (halite) host rock. The FEPs and other supporting information have been entered into a “SaltFEP” Database. The generic salt repository FEPs include consideration of relevant FEPs from a number of U.S., Dutch, German, and international FEP lists and should be a suitable starting point for any repository program in salt host rock. The salt FEP catalogue and database employ a FEP classification matrix approach that is based on the concept that a FEP is typically a process or event acting upon or within a feature. The FEP matrix provides a two-dimensional structure consisting of a Features/Components axis that defines the “rows” and a Processes/Events axis that defines the “columns” of the matrix. The design of the FEP classification matrix is consistent with repository performance assessment – the Features/Components axis is organized vertically to generally correspond to the direction of potential radionuclide migration (from the waste to the biosphere) and the Processes/Events axis is designed to represent the common two-way couplings between thermal processes and other processes (such as thermal-mechanical or thermal-hydrologic processes). Related FEPs can be easily identified – related FEPs will typically be grouped in a single matrix cell or aligned along a common row (Feature/Component) or column (Process/Event). The online SaltFEP database can be downloaded from www.saltfep.org. It contains the FEP matrix, the FEPs, and the associated processes for each FEP. It provides a starting point to create and document site-specific individual FEPs. Furthermore, the FEP matrix is connected to the Salt Knowledge Archive (SKA), a database of about 20,000 references and documents representing the historical knowledge on radioactive disposal in salt. This work is the result of an ongoing collaboration between researchers in the U.S., the Netherlands, and Germany, and supports the NEA Salt Club Mandate. It builds upon prior work which is documented.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Characterization of Long-Term Service Coal Combustion Power Plant Extreme Environment Materials

The objective of this DOE-sponsored project was to develop a comprehensive database of mechanical properties, alloy microstructures, and to a lesser extent, the oxidation/corrosion behaviors of coal-fired power plant components, such as boiler tubing, steam headers, and steam piping, which had been in service for at least 100,000 operating hours (preferably more than 200,000 operating hours) under the operating conditions of high temperatures and high mechanical stresses where creep, fatigue, steam-side oxidation, and fireside corrosion were life-limiting factors. The components included in this database consisted of ferritic steels, creep strength enhanced ferritic (CSEF) steels, and 300-series H-grade stainless steels, as well as dissimilar metal welds (DMWs) among these types of materials. As a result of extensive metallurgical characterization and mechanical testing performed in this project, a comprehensive database on mechanical properties and detailed quantitative microstructural information was successfully developed for several long-term serviced EEM components. Such a database can be used by material research communities to develop, calibrate, refine, and validate mechanical behaviors, models, and other assessment tools for accurate prediction of remaining life of major components under similar EEM operating conditions.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2022

Progress Report on SFR Metallic Fuel Data Qualification

This report summarizes the progress of SFR metallic fuel qualification related activities, which are focused on providing quality assurance relevant information applicable to experiments irradiated during the Integral Fast Reactor (IFR) program. A background of metallic fuel performance data and the associated databases, including the EBR-II Fuels Irradiation & Physics Database (FIPD) and Out-of-Pile Transient Database (OPTD), is included. The legacy data in the databases, including as-built, post-irradiation examination (PIE), operating parameters, and out-of-pile experiment data are introduced. The SFR metallic fuel Quality Assurance Program Plan (QAPP) and its implementation to qualify these legacy data is described in detail. Important PIE data QA documents and the specifications of four types of PIE measurements (contact profilometry, laser profilometry, neutron radiography and gamma scan) are provided. Examples of the implementation of the QAPP to qualify each of those types of PIE data are provided.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Quality Assessment of Molten Salt Thermochemical Property Data

Recommendations are made to enhance user confidence in property values included in the Molten Salt Thermal Database-Thermochemical Properties (MSTDB-TC). Specifically, it is recommended that all available measured values be included with a transparent assessment of each to arrive at a preferred value for each salt composition in the database. The use of a quality assessment and ranking system is recommended for data and property values compiled in the MSTDB-TC that is analogous to the approach developed for data and property values included in the MSTDB-Thermophysical Properties (TP) database. The approach includes assessments of five features of data collection: the method used to measure the property value, calibration of devices used to make the measurements, the quantified uncertainty of the results, control of the environmental conditions, and determination of the salt composition. The quality of each facet is ranked as High, Moderate, or Incomplete based on objective metrics. The overall quality of the dataset is ranked based on rankings of the five data features and traceability of the reported property value, with rankings of A and B indicating the data and property value are deemed suitable for quantitative use, C indicating the value is computed and not validated and U indicating uncertainties restrict the recommended use to qualitative purposes. Sufficient information should be provided or cited within each data set included in the database to verify the reported property value. The recommended approach is suitable for assessing data already in the MSTDB-TC and screening new entries.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

DFTT report for Signal Analysis (2023)

The Detection Framework Testbed and Toolkit (DFTT) is a database and associated java programs intended to facilitate the development and testing of algorithms for operating suites of correlation and subspace detectors. This framework is a generalization of the system described in Harris and Dodge (2011) and Dodge and Harris (2016). DFTT allows retrospective processing of sequences of data using various system configurations. Results are saved in a database, so it is easy to compare the results obtained using different configurations of the system. The principal software components of DFTT are the ConfigCreator, the framework_runner and the Builder program. ConfigCreator assembles the files necessary to define a particular configuration used for processing. The framework_runner operates suites of detectors and saves the results in an Oracle™ database. Builder allows visual examination of templates and detected signals and allows editing and creation of detectors. In addition, there are tools for importing continuous data into the database. DFTT is licensed under the MIT license and has LLNL release number LLNL-CODE-801881. In the current fiscal year DFTT has been used in two different projects. The first (NTL-funded) project investigates the feasibility of screening nuisance detections using correlation detectors. The second (GNEM-funded) project seeks to extend the results of Harris and Dodge (2021) to 3- D sources and multiple stations. Each of these efforts required that additional functionality be added to DFTT. In this report I will summarize the enhancements to DFTT in the context of the relevant research effort.

58 GEOSCIENCES↗
DOE OSTI2024

AIACHNE's contribution for Nuclear Energy Agency Working Party on International Nuclear Data Evaluation Co-operation Subgroup 50

The AIACHNE (AI/ML Informed cAlifornium CHi Nuclear data Experiment) project aims at designing an experiment for the 252 Cf Prompt Fission Neutron Spectrum (PFNS) that explores systematic biases in an experimental database retrieved from the EXFOR databases. To that end, machine learning (ML) methods were applied to pint-point measurement features likely related to bia. From that information, we selected a feature that should be explored by the AIACHNE experiment. Measurement features are metadata encapsulating all pertinent information about the physical measurement and analysis techniques. Examples are, for instance, what neutron and fission detectors were used for the physical metadata, and what background reduction techniques were employed for analysis techniques. Such metadata were retrieved both from EXFOR entries as well as the literature of data sets described in detail in Ref. [2]. The prerequisite for applying machine learning techniques is casting the metadata into a format that can be parsed by the algorithm. This step might seem trivial but requires to find a unique language where metadata that carry the same physics meaning across several experiments must have the same identifier. One example is, for instance, the neutron detector. As seen in Figure 1, the machine learning code identified the use of 6 Li detectors as being related to bias in some datasets of the AIACHNE 252 Cf PFNS experimental database. In fact, here are several experiments that used neutron detectors containing 6Li in the database, for instance for the example below. EXFOR format has a unique keywords describing detectors such as “SCIN” or “GLASD”. One may think that these keywords are already sufficient descriptors for ML to uniquely find an issue. However, “SCIN” (used for [3, 4]) and “GLASD” (used for [5]) fail to inform the algorithm what is the active material in the detector. And, the key common issue leading to bias in 252 Cf related to neutron detectors is not whether it is a glass detector or a scintillator. No, the issue is that 6 Li was within both detector types and that even small mistakes in the detector response functions around approximately 200 keV are amplified by the 6 Li(n,α) resonance there leading to bias in data as highlighted in Fig. 1 and Ref. [1]. Hence, the features describing the neutron detector must call out the active material in the detector, rather than the existing EXFOR detector keyword, that the ML algorithm can find physically meaningful features related to bias. The AIACHNE team used a precursor of the WPEC (Working Party on International Nuclear Data Evaluation Co-operation) SG(Subgroup)-50 format to store the metadata for the ML analysis.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Schema Elements for Granta Annual Report: FY2024

Granta: Materials Intelligence (Granta: MI) is a commercial database software distributed by Ansys, Inc. that is utilized by the Nuclear Security Enterprise (NSE) to organize and store relevant materials data. Lack of standard and well-documented database schema is the primary obstacle to an NSE materials data management solution, so the objective of this project is to create and document such a schema. In FY21, an approach for designing, documenting, and managing a standard database schema was described based on the creation of schema elements (collections of attributes used to describe particular aspects of the data) to be used as building blocks for creating various database tables without duplication. In FY22, these methods were applied through a multi-site collaboration to create and document the schema elements necessary to build a thermogravimetric analysis (TGA) testing table. In FY23 the schema was expanded to include elements for a differential scanning calorimetry (DSC) table, along with schema for supporting metadata tables including Instruments, Projects, Documents, and Testing Series. In FY24 the following progress was made, again through multi-site collaboration: • The existing schema elements were modified to accommodate thermomechanical analysis (TMA) data, and a table, Test Data: TMA, was created for managing TMA data. • The elements necessary for the following additive manufacturing (AM) data tables (directed at data specific to selective laser sintering AM technology) were created: • AM Builds • AM Processes • AM Part Designs • Built AM Parts • AM Feedstock Materials • AM Feedstock Material Batches • The elements necessary for creating a Calibrated Material Models table were created, and the Calibrated Material Models table was created. In FY25 the existing schema will be deployed on the production enterprise Granta instance on the enterprise secure network. Schema elements will be appended, and new elements created as necessary, to allow the creation of tables specifically to support materials testing, AM process development, and design and analysis for modernization programs.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Quality Ranking of Unary Chloride Salt Property Data Included in MSTDB-TP

Molten salt reactor developers rely on thermal property data to design, license and operate the reactors. The Molten Salt Thermal Database-Thermophysical Properties (MSTDB-TP) was established under the DOE Nuclear Energy Advanced Modeling and Simulation (NEAMS) program and is managed by Oak Ridge National Laboratory to serve as a single source of thermophysical property values measured for a wide variety of molten salt systems for use by researchers, molten salt reactor developers, and regulators. These properties include density, viscosity and thermal diffusivity and conductivity. Published measurements of molten salt properties are lacking for many salts of interest and the data that are available are often inconsistent. This creates a challenge for MSR developers when determining which property values to use when designing their reactors. It is the purpose of this work to apply a consistent ranking system to all data entries that indicates the quality of property values listed in the database. These rankings will be the technical basis for down-selections by the database developers and alert users about the quality of the available property values. MSTDB-TP collects all available property data and indicates preferred data sets or correlations. However, all available data sets are included in the database. Quality assessments and rankings are being applied to data in MSTDB-TP to provide an indication of the quality of each data set independent of consistency with other data. Previous reports detailed the ranking system that was followed and assessments of unary fluoride data sets. Documentation of the quality of data in MSTDB-TP was continued by reviewing and assessing all available sources of density, viscosity and thermal diffusivity or conductivity values for unary chloride salts in MSTDB-TP V3.0 using the same criteria.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

CLEAP Project: OR-SAGE Analysis for MT, UT, and CO States

The OR-SAGE tool is designed to use industry-accepted practices in screening sites and then employ the proper array of data sources through the considerable computational capabilities of GIS technology available at ORNL. The tool was developed to screen the potential for NPP siting on a national and regional basis. However, because of the tool granularity, it is often focused specifically on the immediate area around user sites of interest. If data center siting parameters can be added to OR-SAGE, the ability to evaluate data center siting on a localized scale will be beneficial.1 More than 60 data sets have been collected and processed by ORNL to develop exclusionary, avoidance, and suitability criteria for screening sites for a variety of power generation types, including nuclear power plants. Available site evaluation parameters include population density, slope, seismic activity, proximity to cooling-water sources, proximity to hazard facilities, avoidance of protected lands and floodplains, susceptibility to landslide hazards, and many others. All siting parameters should be considered as flags to inform siting decisions and should not be used to rule in or rule out any NPP site. Once data center siting parameters are identified, appropriate data sets will be collected and processed. The OR-SAGE process is very versatile. Essentially, OR-SAGE is a visual, relational database. The database partitions the contiguous United States, a total of 720 million hectares (~1.8 billion acres), into 100-m by 100-m (1 hectare or ~2.5 acre) cells. The database is tracking just under 700 million individual land cells. Successive suitability criterion is applied to each cell in the database. User-specified thresholds can be applied to each siting parameter data layer. In this manner, a variety of scenarios can be quickly and thoroughly evaluated. Data can be added and/or revised within OR-SAGE to address user interests. Siting security assessment capability is currently being added to OR-SAGE. Security is expected to be of concern at data centers whether it is collocated with a nuclear power generating technology or not. If data center is collocated with a nuclear power generating source, the security threat attractiveness level of both will likely increase. It will be of additional benefit if a potential data center site is also assessed for security vulnerability.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

2024 Buildings Technology Baseline: Dataset Documentation

The Buildings Technology Baseline is a curated and regularly updated dataset of current and projected performance, retail, and installed price data for all major building energy technologies needed to enable cost/benefit analyses. Building technology analyses require an up-to-date understanding of installation costs and cost-effectiveness of key building energy efficiency technologies. The dataset was assembled by Guidehouse during fiscal year 2024. Data was gathered from the 2024 National Residential Efficiency Measures Database (NREMDB), the 2023 Energy Information Administration Updated Buildings Sector Appliance and Equipment Costs and Efficiencies ("EIA Building Data Report"), DOE Lighting Market Model, the 2023 RSMeans database, and the 2020 Grid-Interactive Efficient Building Technology Cost, Performance, and Lifetime Characteristics ("GEB Data Report"), Lawrence Berkeley National Laboratory, various literature, as well as new data from online retailers, stakeholder interviews, and contractor databases in 2023 and 2024. The dataset has been reviewed by subject matter experts at NREL and DOE. The 2024 dataset release is intended to be a starting point for interested users to provide feedback. This database is not intended to provide specific cost estimates for a specific project. The cost estimates do not include any rebates or tax incentives that may be available for the measures. Rather, it is meant to help determine which measures may be more cost-effective. The National Renewable Energy Laboratory (NREL) makes every effort to ensure accuracy of the data; however, NREL does not assume any legal liability or responsibility for the accuracy or completeness of the information.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

An Integrated ML/AI Framework for Digitizing, Structuring and Searching DOE U-TRU-Fuels Data with Gap Analysis of Non-DOE Records

The U.S. Department of Energy (DOE) Advanced Fuels Campaign (AFC) is advancing transmutation fuel technologies to reduce long-lived radioactive waste by converting minor actinides into shorter-lived or stable elements through irradiation in sodium-cooled fast reactors. Key experiments such as AFC-1, AFC-2, FUels for the transmutation of Trans-URanium elements In phéniX (FUTURIX)-Fortes Teneurs en Actinides (FTA), and Experimental Breeder Reactor-II (EBR-II) X501 have provided fuel fabrication, irradiation, and performance data on various transuranic-bearing fuel forms. This report documents the creation of an artificial-intelligence assisted database, which has consolidated all DOE-owned data related to Transuranic (TRU)-bearing fuel experiments and stored across it across both the Idaho National Laboratory (INL) Nuclear Data Management and Analysis System and the INL high performance computing (HPC) infrastructure. A dedicated webpage, hosted on the INL HPC system, has been developed to support role-based access and data interaction. The database architecture allows researchers to navigate large, heterogeneous archives with far greater speed and accuracy than manual search and lays the foundation for future expansion into multimodal nuclear materials analysis environments. The database represents a major step towards a nationally integrated fuels database utilizing artificial intelligence tools.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Report priority gaps in high temperature thermodynamic data (Interim Progress Report)

This interim progress report (Level 4 Milestone Number M4SF-26LL010203023) summarizes research conducted at Lawrence Livermore National Laboratory (LLNL) within the Argillite Host Rock Properties & Processes SF-26LL01020302. Our focus is to assess gaps in data availability and understanding for radionuclide thermodynamics within the context of a “hot repository” concept and expand SUPCRT-NE database development to address higher temperatures needed for a DPC DGR disposal concept. The database is intended to inform the argillite GDSA baseline model. The leading European thermochemical database (Thermochimie) is only applicable to temperatures below 80°C. Thus, a US effort to integrate and expand upon other international thermodynamics database efforts is needed, particular if a “hot repository” concept moves forward.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Seeing values for LSST strategy simulations

The opsim4 operations simulation program for the LSST astronomical survey uses a database of seeing values covering the range of times to besimulated. Idescribethe creation of such a database using Dual Image Motion Monitor(DIMM)datacollected at Cerro Pachon from 2004-03-17 to 2019-10-07. In times during which the data overlap, I compare the distribution of DIMM seeing values to the seeing measured in DECamimages,takenatasite 10kmaway. Becauseinstrumentalproblemsinthe DIMMmay indicate unreliablemeasurements,cutsonimagequality(asindicatedby the measured Strehlratio)wereexplored. TheDIMMhassignificantgaps,soImodel thedata(withandwithoutcutsonStrehlratio)andgenerateartificialdatainthegaps according to the model. The model consists of a sinusoidal variation with a period of one year, an autoregressive (AR1) model for variations in mean seeing from one night to the next, and another AR1 model for variations on a 5 minute timescale. I create four databases according to thisprocedure, twobasedonDIMMdatastarting 2006-01-01 (with and without a Strehl ratio cut), and two starting 2009-01-01. I then run opsim simulations using each, and an otherwise identical simulation using the default seeing database, and explore the differences

Neilsen, Eric H. [Fermilab]↗
DOE OSTI2024

Hydropower Infrastructure – LAkes, Reservoirs, and RIvers (HILARRI)

HILARRI is a database of links between major datasets of operational hydropower dams and powerplants, and inland water bodies. These connections are critical for conducting large-scale analysis of hydropower infrastructure and their associated natural and engineered water systems. Features include: – Dams from the National Inventory of Dams (2024) and the Global Reservoir and Dam Database (GRanD v1.3) – Hydropower plants from the Existing Hydropower Assets dataset (EHA 2024) These hydropower infrastructure features are linked to several major datasets that provide hydrologic and hydraulic information relevant for analysis of hydropower systems that includes the integral water resources. That information comes from: – Products from the National Hydrography Dataset (NHD) – NHDPlusV2 Medium Resolution river network flowlines, – NHD waterbodies (limited to lakes and reservoirs), – NHD Watershed Boundary Dataset (HUC12-level for the Conterminous United States (CONUS)) – NHD High Resolution waterbodies – HydroLAKES water bodies (lakes and reservoirs) – LAGOS-US lakes and reservoirs – EPA National Lakes Assessment (2007, 2012, 2017, and 2022) – The Reservoir Sedimentation Database (RESSED) Unique identifiers are used to facilitate joining to the original full datasets. For example, characteristics of NHD flowlines such as estimated average flow rate can be joined from the NHDPlusV2 dataset to a dam or power plant listed in HILARRI based on the ID field, “COMID”, that is common to both datasets. HILARRI only includes basic information about identifiers, location, and data quality or usage notes. It does not contain the attributes or time series data associated with these sites. The HILARRI dataset incorporates information from several datasets to facilitate more effective and accurate analysis of hydropower infrastructure and their associated waterbodies. For example, dams were checked against the most recent American Rivers Dam Removal Database to identify and flag facilities that may no longer exist. Additionally, dams that are listed multiple times in the NID are identified and flagged to avoid double-counting when analyzing and summarizing information. Other quality flags include certainty of operational hydropower (i.e., if one or more datasets indicates hydropower at a particular location), whether an associated water body is accurate or composed of multiple polygons, or whether there is a known issue with reported characteristics in one of the underlying datasets. These additional data flags are designed to increase confidence in data usage for individual to large-scale analyses.

13 HYDRO ENERGY↗
DOE OSTI2018

Daily Temperature and Precipitation Data for 223 Former-USSR Stations (NDP-040)

The stations in this dataset are considered by RIHMI to comprise one of the best networks suitable for temperature and precipitation monitoring over the the former-USSR. Factors involved in choosing these 223 stations included length or record, amount of missing data, and achieving reasonably good geographic coverage. There are indeed many more stations with daily data over this part of the world, and hundreds more station records are available through NOAA's Global Historical Climatology Network - Daily (GHCND) database. The 223 stations comprising this database are included in GHCND, but different data processing, updating, and quality assurance methods/checks mean that the agreement between records will vary depending on the station. The relative quality and accuracy of the common station records in the two databases also cannot be easily assessed. As of this writing, most of the common stations contained in the GHCND have more recent records, but not necessarily records starting as early as the records available here. This database contains four variables: daily mean, minimum, and maximum temperature, and daily total precipitation (liquid equivalent). Temperature were taken three times a day from 1881-1935, four times a day from 1936-65, and eight times a day since 1966. Daily mean temperature is defined as the average of all observations for each calendar day. Daily maximum/minimum temperatures are derived from maximum/minimum thermometer measurements. See the measurement description file for further details. Daily precipitation totals are also available (to the nearest tenth of a millimeter) for each station. Throughout the record, daily precipitation is defined as the total amount of precipitation recorded during a 24-h period, snowfall being converted to a liquid total by melting the snow in the gauge. From 1936 on, rain gauges were checked several times each day; the cumulative total of all observations during a calendar day was presumably used as the daily total. Again, see the measurement description file for further details. For access to the data files, click this link to the CDIAC data transition website: http://cdiac.ess-dive.lbl.gov/ndps/ndp040.html

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↗