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DOE OSTI2016

CTF Preprocessor User's Manual

This document describes how a user should go about using the CTF pre- processor tool to create an input deck for modeling rod-bundle geometry in CTF. The tool was designed to generate input decks in a quick and less error-prone manner for CTF. The pre-processor is a completely independent utility, written in Fortran, that takes a reduced amount of input from the user. The information that the user must supply is basic information on bundle geometry, such as rod pitch, clad thickness, and axial location of spacer grids--the pre-processor takes this basic information and determines channel placement and connection information to be written to the input deck, which is the most time-consuming and error-prone segment of creating a deck. Creation of the model is also more intuitive, as the user can specify assembly and water-tube placement using visual maps instead of having to place them by determining channel/channel and rod/channel connections. As an example of the benefit of the pre-processor, a quarter-core model that contains 500,000 scalar-mesh cells was read into CTF from an input deck containing 200,000 lines of data. This 200,000 line input deck was produced automatically from a set of pre-processor decks that contained only 300 lines of data.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2018

NEAMS Workbench 1.0 Beta

The mission of the US Department of Energy (DOE) Nuclear Energy Advanced Modeling and Simulation (NEAMS) Program is to develop, apply, deploy, and support state-of-the-art predictive modeling and simulation tools for the design and analysis of current and future nuclear energy systems. This is accomplished using computing architectures that range from laptops to leadership-class facilities. The NEAMS Workbench is a new initiative that will facilitate the transition from conventional tools to high-fidelity tools by providing a common user interface for model creation, review, execution, output review, and visualization for integrated codes. The Workbench can use common user input, including engineering scale specifications that are expanded into application-specific input requirements through the use of customizable templates. The templating process enables multi-fidelity analysis of a system from a common set of input data. Additionally, the common user input processor can provide an enhanced alternative application input that provides additional conveniences over the native input, especially for legacy codes. Expansion of the integrated codes and application templates available in the Workbench will broaden the NEAMS user community and will facilitate system analysis and design. Current and planned capabilities of the NEAMS Workbench are detailed here.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Neutronic Design and Analysis of the Holos-Quad Concept

HolosGen, LLC is proposing a highly innovative micro-reactor concept targeting both civilian and military applications. It consists of an advanced helium-cooled gas reactor using a turbojet-type turbine and compressor to achieve a highly condensed reactor that fits into a commercial ISO container. The Holos-Quad concept being considered for civilian applications will generate 22MWt, using four Subcritical Power Modules (SPMs) that fit into one 40-foot ISO container. It is a high-temperature gas-cooled reactor concept using TRISO fuel distributed in graphite hexagonal blocks, cooled with helium gas at 7MPa and a high outlet temperature. In FY2019, HolosGen, LLC received an ARPA-E MEITNER grant to team up with the Argonne National Laboratory (ANL) design team to demonstrate the feasibility of the Holos-Quad concept in terms of neutronics. In particular, the evaluation study of the Holos design features and alternative options for reactivity control was considered. Through these tasks, the ANL design team has been working on demonstrating the feasibility of the Holos-Quad concept in terms of neutronics to help advance the core design of the Holos-Quad. A rigorous design approach was employed for the Holos-Quad in order to ensure that the input space is fully investigated, and that the best solutions are considered. The first step of this approach consisted of properly defining the Holos-Quad design problem by identifying the competitive design objectives: its economic performance is maximized with longer lifetime and its transportability to remote locations is maximized with reduced weight. The design problem also required to identify the operational constraints (in terms of thermal hydraulics, reactivity feedback and shutdown margins) and the input parameters that could be varied. In the second step, a sensitivity analysis was performed to enable preliminary investigation of the input space to identify the correlations among input and output variables. It allowed, for instance, to find input parameters that have relatively poor impact on the core performance and that could be removed from the optimization problem to accelerate its convergence. This sensitivity analysis showed that the design problem defined is highly constrained since only 117 solutions out of 1,000 simulated cores met all the design constraints. The design optimization was performed in the third step employing a genetic algorithm to effectively explore this highly constrained input space and find global optimal solutions. More than 2,500 cores were fully evaluated to converge to a Pareto Frontier of optimum solutions providing best compromise between the life-time target and the total core weight, as shown in Figure 1. The main benefit of this approach was to reduce the human effort and to be able to automatically re-run the optimization problem when changing the design problem. The multi-objective optimization identified various core solutions that would be optimum solutions for different types of applications.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2019

NEAMS Workbench Status and Capabilities

The mission of the US Department of Energy’s Nuclear Energy Advanced Modeling and Simulation (NEAMS) program is to develop, apply, deploy, and support state-of-the-art predictive modeling and simulation tools for the design and analysis of current and future nuclear energy systems. This is accomplished by using computing architectures that range from laptops to leadership-class facilities. The NEAMS Workbench is a new initiative that will facilitate the transition from conventional tools to high fidelity tools by providing a common user interface for model creation, review, execution, output review, and visualization for integrated codes. The Workbench can use common user input, including engineering-scale specifications that are expanded into application-specific input requirements through the use of customizable templates. The templating process can enable multifidelity analysis of a system from a common set of input data. Additionally, the common user input processor can provide an enhanced alternative application input that provides additional conveniences over the native input, especially for legacy codes. Expansion of the integrated codes and application templates available in the Workbench will broaden the NEAMS user community and will facilitate system analysis and design. Current and planned capabilities of the NEAMS Workbench are detailed here.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2020

Risk Importance Ranking of Fire Data Parameters to Enhance Fire PRA Model Realism

Fire is historically and analytically a significant contributor to nuclear power plant risk. The level of fire risk and the methods, tools and data for modeling this risk is highly debated by experts. One area of debate is the input data used in fire modeling and how to deal with this data’s high uncertainty. This report outlines initial work performed for determining the key parameters causing this uncertainty and how it propagates into nuclear power plant models. This research paves the way for the development of methods to reduce fire data uncertainty used in modeling. The Nuclear Regulatory Commission has mandated that nuclear power plants perform fire risk modeling. However, there are several issues with the current risk modeling implementation that affect the results. Approved modeling methods can be overly conservative and often do not match plant experience. Also, the data used in the modeling can have high uncertainties and is influenced by expert judgement. To evaluate input data uncertainty, researchers performed an initial review of several fire experiments done at Sandia National Laboratories. Uncertainties for fire data can come from many sources, such as experiment design constraints, environmental conditions, or other plant-specific aspects. There are many different significant and insignificant parameters driving the uncertainty. Additionally, the uncertainty of the different input data used in the fire modeling could have a significant or insignificant effect on the entire plant risk. A four-step methodology was developed to perform Integrated Probabilistic Risk Assessment Importance Ranking. A demonstration case using these steps was set up and three of the four steps were completed in fiscal year (FY) 2019 and the fourth step done FY 2020. These steps are: 1. The qualitative analysis of potential sources was conducted with the following items identified for the demonstration. • Maximum heat release rate • Time to maximum heat release rate • Duration of max heat release rate • Time to decay • Thermal conductivity of concrete • Specific heat of concrete • Density of concrete • Cable jacket thickness 2. A quantitative characterization of dominant sources of uncertainty was performed. A list of distributions and determined values of the dominant sources is shown in Appendix A. 3. A quantitative screening of the potential sources of uncertainty using Morris Elementary Effects Analysis was completed. An experimental model using the physics-based fire modeling tool Fire Dynamics Simulator was developed and coupled with the Risk Analysis Virtual Environment. The Morris analysis identified at least two parameters that can be eliminated as significant contributors (specific heat of concrete and cable jacket thickness). 4. Global importance measure (Global IM) analysis to generate a comprehensive ranking based on their influence on the plant risk. In this research, a moment-independent Global IM is used since it can address (a) uncertainty in the input parameters of the fire model, (b) uncertainty in the risk outputs, and (c) non-linearity and interactions among input parameters in the fire model, more accurately than the correlation-based and variance-based global methods. The observations from the research showed that, depending on the initial and boundary conditions of the fire scenarios, fire-induced damage could have a very small probability and could be dominated by the tail of the uncertainty distribution; hence, the accuracy of the correlation-based and variance-based methods is questionable. The moment-independent Global IM analysis in this research provides a better understanding of how experimental uncertainty data affects industry’s plant models and where improvements in that data will have the largest benefit for improving fire modeling accuracy in causing core damage. Among the five unscreened parameters obtained from the Morris EE analysis, the Global IM analysis results for the case study indicated that max heat release rate and fire location are the most important parameters. The report also outlines benefits of using a unified computational platform that integrates the underlying simulations (e.g., a fire progression model), quantitative screening (using the Morris EE method), and the Global IM analysis. A unified platform can (i) facilitate the ranking of input parameters considering multiple key fire scenarios simultaneously, rather than considering one scenario at a time, (ii) contribute to more explicit and accurate treatment of dependencies at multiple levels of Fire PRA, (iii) facilitate the sampling-based uncertainty quantification for Fire PRA, and (iv) help generating both “industry-wide” and "plant-specific" ranking of uncertainty sources in Fire PRA. Future research should be done to include additional parameters such as detection/suppression or cable fire spread. Adding a PRA software such as SAPHIRE to the RAVEN platform would help with plant model integration and improve treatment of fire-induced dependency. The I-PRA risk importance ranking methodology offered in this report can provide valuable information for efficiently (a) enhancing the realism of Fire PRA for existing plants and (b) supporting the development of Dynamic Fire PRA for advanced reactors and new plants.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

CTF User's Manual (V.4.2)

This document describes how a user should go about using the CTF pre-processor tool to create an input deck for modeling rod-bundle geometry in CTF. The tool was designed to generate input decks in a quick and less error-prone manner for CTF. The pre-processor is a completely independent utility, written in Fortran, that takes a reduced amount of input from the user. The information that the user must supply is basic information on bundle geometry, such as rod pitch, clad thickness, and axial location of spacer grids - the pre-processor takes this basic information and determines channel placement and connection information to be written to the input deck, which is the most time-consuming and error-prone segment of creating a deck. Creation of the model is also more intuitive, as the user can specify assembly and water-tube placement using visual maps instead of having to place them by determining channel/channel and rod/channel connections. As an example of the benefit of the pre-processor, a quarter-core model that contains 500,000 scalar-mesh cells was read into CTF from an input deck containing 200,000 lines of data. This 200,000 line input deck was produced automatically from a set of pre-processor decks that contained only 300 lines of data.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2020

NEAMS Workbench Status and Capabilities

The mission of the US Department of Energy’s Nuclear Energy Advanced Modeling and Simulation (NEAMS) program is to develop, apply, deploy, and support state-of-the-art predictive modeling and simulation tools for the design and analysis of current and future nuclear energy systems. This is accomplished by using computing architectures that range from laptops to leadership-class facilities. The NEAMS Workbench is a new initiative that will facilitate the transition from conventional tools to highfidelity tools by providing a common user interface for model creation, review, execution, output review, and visualization for integrated codes. The Workbench can use common user input, including engineering-scale specifications that are expanded into application-specific input requirements through the use of customizable templates. The templating process can enable multifidelity analysis of a system from a common set of input data. Additionally, the common user input processor can provide an enhanced alternative application input that provides additional conveniences compared with native input, especially for legacy codes. Expansion of the integrated codes and application templates available in the Workbench will broaden the NEAMS user community and will facilitate system analysis and design. Current and planned capabilities of the NEAMS Workbench are detailed herein.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

Sensitivity and Uncertainty of the IFR-1 BISON Benchmark

The fuel performance code BISON is being used to evaluate metallic fuel for a new fast-spectrum test reactor called the Versatile Test Reactor, which is being considered by the US Department of Energy. To quantify the accuracy of BISON predictions, researchers at Oak Ridge National Laboratory have been developing a series of benchmarks based on legacy metallic fuel experiments. As part of this effort, the sensitivity of BISON predictions to variations in model inputs and the uncertainties associated with BISON predictions must be established. This report summarizes efforts to perform a comprehensive sensitivity analysis (SA) and uncertainty quantification (UQ) on a benchmark based on the IFR-1 experiment. For the SA, at least one input was chosen from every BISON model and physics module used in the benchmark. The inputs were varied individually in a series of BISON simulations. The resulting variations in benchmark predictions were normalized to calculate sensitivities. The strongest sensitivities were identified and used to inform input selections for the UQ. The UQ was performed using the Monte Carlo UQ method. A literature review was conducted to estimate uncertainty distributions for the selected inputs, and values were sampled randomly from each distribution in a series of BISON simulations. Variations in the benchmark predictions were used to estimate uncertainty distributions and confidence intervals. It was found that nearly 100% of benchmark predictions matched the corresponding legacy values within the confidence intervals. However, this is at least partially because of the wide confidence intervals associated with the benchmark predictions. The uncertainty contributions of assumptions in the benchmark, experimental uncertainties, and BISON models were quantified. Some analysis was performed to identify inputs that contributed to the uncertainties. Finally, recommendations are made for future benchmark development and future BISON development.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

Using CUBIT to Create Unstructured Mesh Models for MCNP Simulations

The Monte Carlo N-Particle (MCNP) transport code version 6 (also known as MCNP6) has the capability for tracking particles on unstructured mesh (UM) geometry models embedded into constructive solid geometry (CSG) cells. This feature has been developed for performing calculations of complex geometry models because creating CSG models is a time-consuming and error-prone process as the complexities of geometries increase. An MCNP UM calculation requires UM geometry input files. The UM capability was originally designed to work with UM models created with the Abaqus/CAE software and ASCII input files that it generates. The Abaqus-formatted input files needed for MCNP UM calculations must have the correct Abaqus syntax and meet the additional MCNP requirements. Several software packages can generate a UM model formatted as an Abaqus input file. Cubit, the Sandia National Laboratory automated mesh generation toolkit, can generate a UM model formatted as an Abaqus input file. However, the Abaqus input files created by Cubit cannot be used for MCNP simulations. A Python script has been developed to convert an Abaqus file created by Cubit to an Abaqus file that the MCNP code can process. This report describes the process of using Cubit to create UM models for MCNP calculations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Athena-I CUBIT Journal Files

The Monte Carlo N-Particle (MCNP)1 transport code version 6 (also known as MCNP6) has the capability for tracking particles on unstructured mesh (UM) geometry models embedded into con structive solid geometry (CSG) cells. This feature has been developed for performing calculations of complex geometry models because manually creating CSG models is a time-consuming and error prone process as the complexities of geometries increase. The UM capability was originally designed to work with UM models created with the Abaqus software and ASCII input files that it generates. The MCNP code version 6.0 and later can process UM models formatted as Abaqus input files. Starting with version 6.3, MCNP can also process HDF5 mesh input files. External codes must be used to generate Abaqus input files for MCNP UM calculations. CUBIT, the Sandia National Laboratory automated mesh generation toolkit, can generate a UM model formatted as an Abaqus input file. However, the Abaqus input files exported from CUBIT cannot be used for MCNP simulations because it lacks the proper syntax. A Python script was developed to convert an Abaqus file created by CUBIT to an Abaqus file format that MCNP can process. Creating UM models for complex geometries is not an easy task. The process of creating UM models in CUBIT for MCNP simulations is detailed in. CUBIT provides several user interface options including a graphical user interface (GUI) and a command line interface. A GUI provides an easy way to use CUBIT without learning the CUBIT command syntax. When using CUBIT with either interface option, command lines are written into an ASCII file known as a journal file; this journal file can be edited and archived so that it can be played back in CUBIT to automatically generate a UM model. This report describes the CUBIT journal files of the UM models developed for Athena-I. The Athena platform, an energy-tuning assembly, was developed to spectrally shape the National Ignition Facility (NIF) deuterium-tritium fusion neutron source to a thermonuclear (fusion) plus prompt fission neutron spectrum with capability to act as a short-pulse neutron source. MCNP6 was used for the Athena experiment design analysis.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2026

The Levelized Cost of Exergy Framework

Exergy is the amount of energy within a substance or within a transfer of energy that can be used to produce work or some other useful output when interacting with some reference environment. Two different energy systems that produce the same output with the same exergetic efficiency must necessarily have the same amount of exergy input, even if the amount of energy input to the two systems is vastly different. For example, a low-grade heat-driven desalination would require far more energy input than a reverse osmosis (RO) plant producing the same amount of water, but if their exergetic efficiencies were the same, they would require the same amount of exergy input. Thus, comparisons between different energy sources on the basis of energy is not always appropriate. Instead, a comparison on a per unit exergy basis provides more insight on the cost-effectiveness of different energy sources and systems. In this presentation, we describe a framework for analyzing the levelized cost of exergy (LCOEx) for both inputs and outputs of various energy systems. Our framework illustrates how the cost per unit exergy of a system's energy source, as well as the exergetic efficiency of the system, greatly affect the cost of the system output. We use the levelized cost of electricity as a benchmark value for LCOEx, due to electricity's ubiquity as an energy source, and because it is relatively inexpensive on a per unit exergy basis. The LCOEx of various heat sources are then compared to the LCOEx of electricity. Medium- and high-grade industrial heat (> 150 degrees C) produced by natural gas tends to have an LCOEx on par with electricity. This is due to the low cost of natural gas, as well as the high exergy content of heat at higher temperatures. Meanwhile, low-grade heat tends to be an expensive exergy source, owing to the low exergy content of the low-grade heat. We first apply our framework to desalination, where RO has come to dominate, due to the low LCOEx of the energy source (electricity) and relatively high exergetic efficiency of RO compared to thermal desalination systems. We then use this framework to highlight an opportunity for dehumidification systems to experience a similar cost improvement as desalination has. If an electrically-driven, high exergetic efficiency dehumidification system were developed (such as the membrane-based dehumidification systems proposed in literature), it would use a low cost exergy source with a high exergetic efficiency and could potentially lower the cost of dehumidification in the way that RO has done for desalination. Finally, we apply our framework to various fuels (natural gas, hydrogen, gasoline, etc.) and energy systems across different sectors (desalination, dehumidification, vehicles, etc.) to understand the variation in the cost of exergy input and exergetic efficiency of different systems and technologies.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2019

Moirai Land Data System (Moirai) v3.0.0

The Moirai Land Data System (Moirai LDS) is designed to produce recent historical land data inputs for the AgLU module of GCAM data system , but the Moirai LDS outputs could also be used by other models/applications. The Moirai are the Greek Fates, and this software is named Moirai to represent the fundamental influence of land data inputs on model outcomes. The primary function of the Moirai LDS is to combine spatially explicit input data (e.g., raster images) with tabular input data (e.g., crop price table) to generate tabular output data for a suite of variables. Some of these outputs replace the data provide by the Global Trade Analysis Project (GTAP), and other data replace and augment the original GCAM GIS processing. The Moirai LDS output data are aggregated by Geographic Land Unit (GLU) within each country. The GLU coverage is an input to the Moirai LDS (as a thematic raster image and an associated CSV file that maps the thematic integers to names), and the GLU boundaries can be determined arbitrarily. Previous versions of GCAM (and Moirai LDS) used only bioclimatic Agro-Ecological Zones (AEZs) and corresponding data that were provided by GTAP, as the GLUs. As a result, some AEZ terminology still exists in the code, but this terminology now refers more generally to GLUs. The Moirai LDS now enables any set of boundaries to be used as GLUs (including AEZs), allowing for more flexible generation of land use region boundaries (defined as the intersection of GLUs with geopolitical regions). The current default set of GLUs is the same set of 235 global watersheds as used by the GCAM water module. The GCAM 5.1 geopolitical regions (32 or 14) are included and used as inputs to Moirai to generate a mapping file between the Moirai outputs, which are at the level of the intersection between the GLUs and the country boundaries, and the geopolitical regions. The diagnostics scripts use this geopolitical region mapping in some cases. Moirai can also recalibrate three of the outputs (crop production, harvested area, and land rent) to a specified year that is the center of a five-year averaging window. No recalibration retains the circa 2000, 7-year average of the source data. The cucrrency-year for land rent can also be specified, and the default is 2001 to match the GTAP data.

Di Vittorio, Alan↗

DOE OSTI2014

Broadband Heating Rate Profile Project (BBHRP) - SGP 1bbhrpripbe1mcfarlane

The objective of the ARM Broadband Heating Rate Profile (BBHRP) Project is to provide a structure for the comprehensive assessment of our ability to model atmospheric radiative transfer for all conditions. Required inputs to BBHRP include surface albedo and profiles of atmospheric state (temperature, humidity), gas concentrations, aerosol properties, and cloud properties. In the past year, the Radiatively Important Parameters Best Estimate (RIPBE) VAP was developed to combine all of the input properties needed for BBHRP into a single gridded input file. Additionally, an interface between the RIPBE input file and the RRTM was developed using the new ARM integrated software development environment (ISDE) and effort was put into developing quality control (qc) flags and provenance information on the BBHRP output files so that analysis of the output would be more straightforward. This new version of BBHRP, sgp1bbhrpripbeC1.c1, uses the RIPBE files as input to RRTM, and calculates broadband SW and LW fluxes and heating rates at 1-min resolution using the independent column approximation. The vertical resolution is 45 m in the lower and middle troposphere to match the input cloud properties, but is at coarser resolution in the upper atmosphere. Unlike previous versions, the vertical grid is the same for both clear-sky and cloudy-sky calculations.

1bbhrpripbe1mcfarlane↗

DOE OSTI2014

Broadband Heating Rate Profile Project (BBHRP) - SGP ripbe1mcfarlane

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2014

Broadband Heating Rate Profile Project (BBHRP) - SGP ripbe370mcfarlane

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2014

Broadband Heating Rate Profile Project (BBHRP) - SGP 1bbhrpripbe1mcfarlane

1bbhrpripbe1mcfarlane↗

DOE OSTI2024

Urban morphology and urban water demand evolution in the Los Angeles region

Detailed description of the dataset sources used in this study, the experimental workflow, and plotting for the paper figures provided at the associated GitHub Meta Repo: https://github.com/IMMM-SFA/Ferencz_et_al_2024_ERL The future water demand projections from this study are hypothetical future water demands that reflect the population and urban land cover changes represented by the scenarios considered. The intent and emphasis of this work is investigating the interactions between population change, evolution of urban morphology, and water demand. These projections are not meant to be likely future demands for specific water providers or the LA region and should not be interpreted as such. The folders contain input and output data for each step of the "Recreate my Experiment" workflow described in the associated GitHub meta-repository as well as data used for plotting Figures for the paper that this dataset supports. Description of each folder's contents and use: Step_1a: All necessary inputs to the associated python script provided on the GitHub repo. Step_1b: All necessary inputs (downscaled population rasters) used by the associated python script provided on the GitHub repo. Original 1-km squared rasters that were downscaled also provided. Step_1c: Urban growth projection rasters corresponding to SSP3 and SSP5 population scenarios are provided in separate subfolders as well as the water provider boundaries used for analysis. Outputs of data processing also provided. Associated python script provided on GitHub. Step_1d: Description of Inputs used by the QGIS Model Builder GUI that automates geospatial processing and clipping the of the high resolution land cover data for each urban land class footprint within a defined polygon boundary. The Model Builder is provided on the GitHub repo and can be used by QGIS. The outputs of this step are in "Clipped Provider Hi Res Landcover". If the user wants to use The Model Builder for different regions of LA or two test our outputs, they will need to download the hi resolution landcover raster listed in the Readme and in Ref [2] of the GitHub Page. Step_1e: All necessary inputs to generate average monthly demand for each water provider. Associated python script on GitHub. Step 2: Output data about land cover metrics (areas and fractions) for each urban land class for each water provider. Associated python script on GitHub. Uses outputs from Step 1d "Clipped Provider Hi Res Landcover" Step 3: Inputs for and Outputs from the urban projection raster analysis Python script on GitHub. The outputs are rasters of urban pixels that were converted to a higher land class and the number of land class units that changed (Values of 1, 2, or 3). For example, a value of 2 could be LC 21 -> 23 or LC 22 -> 24. These maps are label "intensification." The other outputs are "urban growth" rasters showing the conversion of non urban to urban land, which are indicated by pixel values of 1. Step 4: Output projections of indoor and outdoor annual and monthly demands for each water provider. These are used for Figures 4 - 7 of the paper. Figures: This folder has data used for plotting Figures 1 through 5. Data for Figures 6 and 7 are sourced directly from folders associated with the Processing and Analysis Steps 1 - 4 and the plotting scripts for Figures 6 and 7 are commented with what folder paths are needed to generate the figures. The GitHub page provides descriptions of how each figure was made and the associated plotting scripts used.

Los Angeles↗

DOE OSTI2026

Xanthos-Lake Dataset

The Xanthos-Lake v1.0 dataset provides the input data, trained machine-learning models, and simulation outputs needed to characterize lake water balance, snow and ice conditions, and mixing-layer temperature within the Xanthos global hydrological modeling framework. The dataset supports lake representation across a wide range of lake sizes and hydroclimatic conditions by combining xLSIM, a basin-specific machine-learning emulator of lake snow, ice, ice-cover fraction, and mixing-layer temperature, with the Xanthos-Lake water-balance model. The archive contains NetCDF datasets used to train and evaluate xLSIM, trained model weights, processed meteorological and lake-property inputs, and basin- and lake-category-specific simulation outputs. These materials are organized into four primary data groups, described below. Snowice_model_inputs: Contains the NetCDF input data used to train xLSIM. The xLSIM machine-learning framework uses three lake-based datasets. The meteorological forcing dataset provides monthly relative humidity, specific humidity, surface wind speed, maximum and minimum air temperature, downward longwave and shortwave radiation, snowfall, surface air pressure, and total precipitation. Lake surface area is included as an additional static predictor. The target-state dataset provides lake ice thickness, snow depth, snow cover, and lake mixing-layer temperature, while a companion lake-surface dataset provides the lake ice-cover fraction. Before training, ice thickness and snow depth are converted from meters to centimeters, mixing-layer temperature is converted from kelvin to degrees Celsius and constrained to nonnegative values, and ice-cover fraction is converted from a fraction to a percentage. The predictor variables are normalized using statistics calculated across the selected lakes and time steps. Snowice_model_outputs: Contains the NetCDF outputs generated by xLSIM. For each basin, xLSIM produces a file containing observed and predicted lake-state variables for the training, validation, and testing periods. The modeled variables include lake ice thickness, snow depth, snow cover, mixing-layer temperature, and lake ice-cover fraction. For basins without a sufficiently persistent snow-and-ice signal, the emulator predicts only mixing-layer temperature. The outputs also include training and validation loss histories, the selected model configuration, identifiers of the lakes used in training, and SHAP-based feature-importance information at the global, lake, and seasonal-regime levels. The trained machine-learning model weights are provided separately within the dataset archive. Together, these files support model evaluation and subsequent coupling with the Xanthos-Lake water-balance framework. XanthosLAKES: Contains the NetCDF input data used by the Xanthos-Lake framework. Monthly meteorological inputs include relative and specific humidity, downward shortwave and longwave radiation, mean, maximum, and minimum air temperature, wind speed, precipitation, snowfall, and surface air pressure. Static lake-property datasets provide lake identifiers, geographic locations, surface area, volume, mean depth, elevation, drainage area, fetch, outlet-routing information, and associated Xanthos grid-cell attributes. Separate bathymetric datasets provide the coefficients of the area–depth and volume–depth relationships for each aggregated lake unit. GLEV-based records provide observed lake surface area and evaporation data used to initialize lake states, define reference conditions, and calibrate and evaluate the model. Xanthos-Lake Outputs: Contains the basin- and lake-category-specific NetCDF outputs generated by Xanthos-Lake. Monthly variables include lake surface area, storage volume, outlet discharge, evaporation rate, evaporation volume, lake–groundwater exchange, lake inflow, ice thickness, snow depth, snow-cover fraction, ice-cover fraction, and mixing-layer temperature. The files also contain lake-specific calibration and validation statistics, including normalized root-mean-square error, mean absolute error, Nash–Sutcliffe efficiency, Kling–Gupta efficiency, and percent bias. Stored calibrated and derived parameters include the weir discharge coefficient, fractional freeboard, groundwater exchange coefficient, reference water level, corresponding reference surface area and storage volume, weir-width adjustment factor, and the fraction of routed inflow entering the lake. Basin identifiers, lake category, simulation period, calibration and validation periods, and parameter-schema information are retained as NetCDF metadata.

Abeshu, Guta [Pacific Northwest National Laborator↗