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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Tree Diversity, Site Index, and Carbon Storage Decrease With Aridity in Douglas-Fir Forests in Western Canada

Forests are important for biodiversity, timber production and carbon accumulation, but these ecosystem services may be impacted by climate change. Field data collected from individual forest types occurring across a climatic gradient can contribute to forecasting these consequences. We examined how changes in temperature, precipitation and aridity affect ecosystem services in 23 mature Douglas-fir (Pseudotsuga menziesii) forests in nine climatic regions across a 900 km gradient in British Columbia, Canada. Using Canadian National Forest Inventory methodology, we assessed richness and diversity of plant functional groups, site index, and above- and below-ground carbon stocks. As aridity increased, ecosystem-level tree species richness declined on average from four to one species, Douglas-fir site index declined from 30 to 15 m, and ecosystem carbon storage decreased from 565 to 222 Mg ha –1 . Tree species richness was positively and herb species richness negatively correlated with carbon storage. Carbon storage by ecosystem compartment was largest in aboveground live tree biomass, declining in the following order: mineral soils > coarse woody debris and dead standing trees > forest floor > small and fine woody debris > understory plants. Mineral soil carbon at depths of 0-15 cm, 15-35 cm, and 35-55 cm increased with increasing mean annual precipitation and decreasing aridity. Our results indicate that as aridity increases and precipitation decreases, tree species richness, site index and carbon storage in existing Douglas-fir forests declines. However, assisted or natural migration of Douglas-fir into more humid regions could be associated with more diverse, productive, carbon-rich forests. This study informs carbon stock vulnerability and provides empirical data essential for carbon stock forecasts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Leaf area index (LAI), Teller site, Seward Peninsula, Alaska, 2022

Leaf area index (LAI) sampled from locations on the Teller MM27 NGEE-Arctic site, Seward Peninsula, Alaska. These data were collected in support of the ongoing NGEE-Arctic and NASA ABoVE data synthesis work. Samples were collected in July 2022 at 100 locations that cover the down slope half of Teller MM27 hillslope. This data package includes sample information and LAI data (.csv), an ESRI shape file (.shp) and Keyhole Markup Language (.kml) that define the location and area of each LAI measurement. Metadata files include data descriptions (_dd.csv) for tabular data, file level metadata (.csv), and methods and the instrument manual (.pdf). The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Summertime methane and carbon dioxide emission rates and associated variables from a national-scale survey of 146 reservoirs in the United States, 2016-2023

Reservoirs are globally important sources of greenhouse gases, but the magnitude of their emissions is highly uncertain. Here we present data for 146 reservoirs from two surveys of reservoir methane and carbon dioxide emissions, one at the regional scale in the midwestern United States and one at the national scale in the conterminous United States, plus data from one reservoir in Washington and another in Puerto Rico. At all reservoirs, ebullitive and diffusive emissions and basic physiochemistry were measured at 15-70 locations during one 22 to 64-hour period during the summers of 2016-2023, with four reservoirs revisited a second time. Concomitant water chemistry measurements were also made at an index site. The dataset is comprised of two geospatial files and seven .csv files containing greenhouse gas emissions, water chemistry, morphology, and other relevant data. These data comprise the largest multi-reservoir emissions dataset ever assembled using consistent measurement methods.

Beaulieu, Jake [University of Notre Dame, IN]↗
DOE OSTI2025

Snow ALbedo eVOlution (SALVO) Campaign Qualitative Characterization of Snowpack Stratigraphy from April - June, 2024 in Utqiagivk, AK

The physical structure of the snowpack is impacted by and impacts the exchange of energy with the atmosphere. Qualitative characterization of snowpack stratigraphy in snow pits provides context for what physical processes are changing the snow (e.g., melt, wind-packing, hoar formation). 36 snowpits were dug as part of the 2024 Snow Albedo Evolution (SALVO) field campaign. Snow grain stratigraphy was recorded using standard International Association of Cryospheric Sciences (IACS) snow grain classification ( https://cryosphericsciences.org/publications/snow-classification/) and hand hardness tests (in order of least hard to hardest: F (fist), 4F (four fingers), 1F (one finger), P (pencil), K (knife), I (ice)). These snowpits were generally co-located with library site albedo measurements and are given the same index (Date, Site, Location, Position) when appropriate (see broadband and spectral albedo data archived from the SALVO II project).

Depth to bottom of snow layer↗
DOE OSTI2022

Classification of Photovoltaic Failures with Hidden Markov Modeling, an Unsupervised Statistical Approach

Failure detection methods are of significant interest for photovoltaic (PV) site operators to help reduce gaps between expected and observed energy generation. Current approaches for field-based fault detection, however, rely on multiple data inputs and can suffer from interpretability issues. In contrast, this work offers an unsupervised statistical approach that leverages hidden Markov models (HMM) to identify failures occurring at PV sites. Using performance index data from 104 sites across the United States, individual PV-HMM models are trained and evaluated for failure detection and transition probabilities. This analysis indicates that the trained PV-HMM models have the highest probability of remaining in their current state (87.1% to 93.5%), whereas the transition probability from normal to failure (6.5%) is lower than the transition from failure to normal (12.9%) states. A comparison of these patterns using both threshold levels and operations and maintenance (O&M) tickets indicate high precision rates of PV-HMMs (median = 82.4%) across all of the sites. Although additional work is needed to assess sensitivities, the PV-HMM methodology demonstrates significant potential for real-time failure detection as well as extensions into predictive maintenance capabilities for PV.

classification↗
DOE OSTI2020

Leaf Area Index (LAI) of vegetation at the Teller site, Seward Peninsula, Alaska, 2017

Measurements of leaf area index (LAI) at 299 locations on the "tall shrub transect" at the NGEE Arctic Teller field site. Measurements were made on 2017-07-27 using a LiCor LAI-2200. The dataset includes 9 files as dGPS locations, raw measurements, processed data and R script used for processing. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Divergent effects of sex and calcium/vitamin D supplementation on serum magnesium and markers of bone structure and function during initial military training

Maintaining Mg status may be important for military recruits, a population that experiences high rates of stress fracture during initial military training (IMT). The objectives of this secondary analysis were to (1) compare dietary Mg intake and serum Mg in female and male recruits pre- and post-IMT, (2) determine whether serum Mg was related to parameters of bone health pre-IMT, and (3) whether Ca and vitamin D supplementation (Ca/vitamin D) during IMT modified serum Mg. Females (n 62) and males (n 51) consumed 2000 mg of Ca and 25 μg of vitamin D/d or placebo during IMT (12 weeks). Dietary Mg intakes were estimated using FFQ, serum Mg was assessed and peripheral quantitative computed tomography was performed on the tibia. Dietary Mg intakes for females and males pre-IMT were below the estimated average requirement and did not change with training. Serum Mg increased during IMT in females (0·06 ± 0·08 mmol/l) compared with males (–0·02 ± 0·10 mmol/l; P < 0·001) and in those consuming Ca/vitamin D (0·05 ± 0·09 mmol/l) compared with placebo (0·001 ± 0·11 mmol/l; P = 0·015). In females, serum Mg was associated with total bone mineral content (BMC, β = 0·367, P = 0·004) and robustness (β = 0·393, P = 0·006) at the distal 4 % site, stress–strain index of the polaris axis (β = 0·334, P = 0·009) and robustness (β = 0·420, P = 0·004) at the 14 % diaphyseal site, and BMC (β = 0·309, P = 0·009) and stress–strain index of the polaris axis (β = 0·314, P = 0·006) at the 66 % diaphyseal site pre-IMT. No significant relationships between serum Mg and bone measures were observed in males. Findings suggest that serum Mg may be modulated by Ca/vitamin D intake and may impact tibial bone health during training in female military recruits.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Leaf-area index of Oak-Hickory Forest at Missouri Ozark (MOFLUX) site: 2007–2022

This data set contains measurements of leaf-area index (LAI) at the Missouri Ozark (MOFLUX) site during growing seasons from 2007-2022. The MOFLUX site is located in the University of Missouri Baskett Forest, a second growth oak-hickory forest situated in the Ozark Border Region of central Missouri, USA. MOFLUX is part of the AmeriFlux network (site ID: US-MOz) and an eddy covariance tower marks the middle of the site (Figure 1). During 2003, 24 circular vegetation plots (each 0.08 hectares) were established within a ~250 meter (m) radius around the tower. The plots were situated 50 m apart along 5 linear transects radiating out from the flux tower base in southeast, south, southwest, west, and northwest directions. There were 5 plots per transect except for the northwest one, which had only 4 due to the presence of a small pond at the terminus. At weekly intervals during the growing season, leaf-area index measurements were taken within each vegetation plot on the 5 transects running from the central flux tower. A plant canopy analyzer (model LAI-2000 Li-Cor Inc., Lincoln NE) was used to make the LAI measurements. We collected samples to enable measurement of the mean LAI of each transect. LAI observations are useful for understanding vegetation phenology, and to aid in the interpretation of ecosystem gas exchange. This dataset contains one data file in comma separate (.csv) format. Additional metadata are provided: one data dictionary and a file-level metadata file in comma separate (.csv) format and a user guide in PDF (*.pdf) format.

ESS-DIVE CSV File Formatting Guidelines Reporting ↗
DOE OSTI2022

LAI, EVI, NDVI, and kNDVI in 23 pantropical forests affected by 21 cyclones

Statement of purpose: Cyclones alter the function and composition of tropical forests, making effects of intensifying cyclones on carbon-rich forests a critical topic of study. Here, we quantified cyclone-induced damage and recovery of 21 cyclone disturbances affecting 23 pantropical forest sites between 1988-2017 utilizing leaf area index (LAI), enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), and transformed NDVI (kNDVI) values from Google Earth Engine. Field observations collected in a meta-analysis (Bomfim et al., 2022, in review) were used to ground-truth and test effects of soil resource availability and disturbance factors on damage and recovery. This meta-analysis also served as the basis to begin vegetation index extraction, utilizing unique site and date combinations, from tropical forests effect by cyclone disturbances. We began collecting NDVI (5km resolution) from the NOAA Climate Data Record (CDR) of AVHRR Normalized Difference Vegetation Index (NDVI), Version 5 data product (Vermote, 2019) for all case studies included, 42. Next, we began extracting Landsat data from Landsat 4, 5, and 8, courtesy of the U.S. Geological Survey, in search of higher resolution data. We selected a 3 by 3 Landsat pixel area, leading to a 90m resolution data extraction. The specific imagery used includes Landsat 4 USGS Landsat 4 TM Collection 1 Tier 1 TOA (top of atmosphere) Reflectance, Landsat 5 USGS Landsat 5 TM (thematic mapper) Collection 1 Tier 1 TOA Reflectance, and Landsat 8 USGS Landsat 8 Collection 1 Tier 1 TOA Reflectance. Within Google Earth Engine, we selected the date and location (latitude and longitude), calculated NDVI, kNDVI, and EVI utilizing Landsat bands (see metadata_NGEE-tropics_cyclones), and extracted post- and pre-cyclone values for each case study to calculate cyclone-induced change in the vegetative index. Due to limited spatial resolution of Landsat remote sensing data, MODIS products were investigated next. First, the MOD13Q1.006 Terra Vegetation Indices 16-Day Global 250m product was used to extract 250m EVI and NDVI (Didan, 2015) and then the MCD15A3H.006 MODIS Leaf Area Index/FPAR 4-Day Global 500m product product was used to extract LAI 500m (Myneni et al., 2015). Pre- and post-cyclone values, change in the vegetative index, and standard deviation for all values are included in the main csv (see case_study_data.csv) for all vegetative indices collected, including LAI 500m, EVI 250m, NDVI 250m, NDVI 90m, kNDVI 90m, EVI 90m, and NDVI 5km. Lastly, recovery values were calculated utilizing a standardization method (see metadata_NGEE-tropics_cyclones) and recovery values for MODIS (see MODIS_recovery.csv) and Landsat (Landsat_recovery.csv) data are included.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Estimating biotic integrity to capture existence value of freshwater ecosystems

The US Environmental Protection Agency (EPA) uses a water quality index (WQI) to estimate benefits of proposed Clean Water Act regulations. The WQI is relevant to human use value, such as recreation, but may not fully capture aspects of nonuse value, such as existence value. Here, we identify an index of biological integrity to supplement the WQI in a forthcoming national stated preference survey that seeks to capture existence value of streams and lakes more accurately within the conterminous United States (CONUS). We used literature and focus group research to evaluate aquatic indices regularly reported by the EPA’s National Aquatic Resource Surveys. We chose an index that quantifies loss in biodiversity as the observed-to-expected (O/E) ratio of taxonomic composition because focus group participants easily understood its meaning and the environmental changes that would result in incremental improvements. However, available datasets of this index do not provide the spatial coverage to account for how conditions near survey respondents affect their willingness to pay for its improvement. Therefore, we modeled and interpolated the values of this index from sampled sites to 1.1 million stream segments and 297,071 lakes across the CONUS to provide the required coverage. The models explained 13 to 36% of the variation in O/E scores and demonstrate how modeling can provide data at the required density for benefits estimation. We close by discussing future work to improve performance of the models and to link biological condition with water quality and habitat models that will allow us to forecast changes resulting from regulatory options.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Soil carbon stocks in temperate grasslands differ strongly across sites but are insensitive to decade-long fertilization

We report enhancing soil carbon (C) storage has the potential to offset human-caused increases in atmospheric CO 2 . Rising CO 2 has occurred concurrently with increasing supply rates of biologically limiting nutrients such as nitrogen (N) and phosphorus (P). However, it is unclear how increased supplies of N and P will alter soil C sequestration, particularly in grasslands, which make up nearly a third of non-agricultural land world-wide. Here, we leverage a globally distributed nutrient addition experiment (the Nutrient Network) to examine how a decade of N and P fertilization (alone and in combination) influenced soil C and N stocks at nine grassland sites spanning the continental United States. We measured changes in bulk soil C and N stocks and in three soil C fractions (light and heavy particulate organic matter, and mineral-associated organic matter fractions). Nutrient amendment had variable effects on soil C and N pools that ranged from strongly positive to strongly negative, while soil C and N pool sizes varied by more than an order of magnitude across sites. Piecewise SEM clarified that small increases in plant C inputs with fertilization did not translate to greater soil C storage. Nevertheless, peak season aboveground plant biomass (but not root biomass or production) was strongly positively related to soil C storage at seven of the nine sites, and across all nine sites, soil C covaried with moisture index and soil mineralogy, regardless of fertilization. Overall, we show that site factors such as moisture index, plant productivity, soil texture, and mineralogy were key predictors of cross-site soil C, while nutrient amendment had weaker and site-specific effects on C sequestration. This suggests that prioritizing the protection of highly productive temperate grasslands is critical for reducing future greenhouse gas losses arising from land use change.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

SIF and Vegetation Indices in the US Midwestern Agroecosystems, 2016-2021, Carbon Monitoring System (CMS)

This dataset contains half-hourly ground solar-induced chlorophyll fluorescence (SIF) and vegetation indices including NDVI, EVI, Red edge chlorophyll index, green chlorophyll index, and photochemical reflectance index at seven crop sites in Nebraska and Illinois for the period 2016-2021. Four sites were located at Eddy Covariance (EC) tower sites (sites US-Ne2, US-Ne3, US-UiB, and US-UiC), and three sites were located on private farms (sites Reifsteck, Rund, and Reinhart). The sites were either miscanthus, corn-soybean rotation or corn-corn-soybean rotation. The spectral data for SIF retrieval and hyperspectral reflectance for vegetation index calculation were collected by the FluoSpec2 system, installed near planting, and uninstalled after harvest to collect whole growing-season data. Raw nadir SIF at 760 nm from different algorithms (sFLD, 3FLD, iFLD, SFM) are included. SFM_nonlinear and SFM_linear represent the Spectral fitting method (SFM) with the assumption that fluorescence and reflectance change with wavelength non-linearly and linearly, respectively. Additional data include two SIF correction factors including calibration coefficient adjustment factor (f_cal_corr_QEPRO) and upscaling nadir SIF to eddy covariance footprint factor (ratio_EC footprint, SIF pixel), and measured FPAR from quantum sensors and Rededge NDVI calculated FPAR. The data are provided in comma-separated values (CSV) format.

Ameriflux↗
DOE OSTI2022

[Fe 4 S 4 ] cubane in sulfite reductases: new insights into bonding properties and reactivity

The dissimilatory sulfite reductase enzyme has very characteristic active site where the substrate binds to an iron site, ligated by a siroheme macrocycle and a thiol directly connected to a [Fe 4 S 4 ] cluster. This arrangement gives the enzyme remarkable efficiency in reducing sulfite and nitrite all the way to hydrogen sulfide and ammonia. For the first time we present a theoretical study where substrate binding modalities and activation are elucidated using active site models containing proton supply side chains and the [Fe 4 S 4 ] cluster. Density functional theory (DFT) was deployed in conjunction with the energy decomposition scheme (as implemented in AMS), the quantum theory of atoms in molecules (QTAIM), and conceptual DFT (cDFT) descriptors. We quantified the role of the electrostatic interactions inside the active site created by the side chains as well as the influence of the [Fe4S4] cluster on the substrate binding. Furthermore, using conceptual DFT results we shed light of the activation process, thus, laying foundation for further mechanistic studies. We found that the bonding of the ligands to the iron complex is dominated by electrostatic interactions, but the presence of the [Fe 4 S 4 ] cubane leads to substantial changes in electronic interaction. The spin state of the cubane, however, affects the binding energy only marginally. The conceptual DFT results show that the presence of the [Fe 4 S 4 ] cubane affects the reactivity of the active site as it is involved in electron transfer. This is corroborated by an increase in the electrophilicity index, thus making the active site more prone to react with the ligands. The interaction energies between the ligand and the siroheme group are also increased upon the presence of the cubane group, thus, suggesting that the siroheme group is not an innocent spectator but plays an active role in the reactivity of the dSIR active site.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Southeast Regional CO 2 Utilization and Storage Acceleration Partnership (SECARB-USA): Data Quality Methodology (4.2.1)

SECARB-USA Deliverable 4.2.1 utilizes the outputs from the Needs Assessment (Subtask 2.1) to develop a data quality methodology. Many types of data are needed to evaluate a site for technical and financial viability. Several inventories of data types have been produced (for example NETL, 2010). The objective here is to organize the data types so that the needs met are specified. From this cross index (Table 1 in the Appendix), it will be possible in future tasks to (1) determine, on a site-specific basis, how much of each data type is required at each stage of a project to meet the need, and conversely (2) to further specify and define the data collection methods applied such that the data are tailored to fit that need. Derivative tables can then be developed to semi-quantitively evaluate the extent to which need is critical for early go/no decision points, or if it is more important than average, requiring faster or larger capitalization and spend to meet the need. In an additional step, the current availability of data for a site can be semi-quantitively assessed. From the table of data criticality and the table of data availability, site-specific cost for meeting the data needs can be determined, and allow a pre-permit spend estimated for a portfolio of projects. All of the data needs to evaluate a site are somewhat interconnected. We used criterion (2) above to determine if the data collection design would have to be modified to meet the need; if this was commonly true a linkage was shown. The application of this cross index to sites in the subsequent tasks will demonstrate that the demand for data types varies site-to-site and project-to-project. Examples of factors to be considered are the complexity of the geology, the injection goals such as rate and duration of the injection, and the types of risk and risk tolerance of key stakeholders. In future tasks the team will compare the demand for data with existing data availability. This will, in turn, determine when data needs to be acquired to support project development. For example, in a project area with complex structure, 3-D seismic data may be needed earlier and more urgently than in an area with simple rock body geometries. In some locations, a 3-D seismic survey has already been collected and can be purchased. In other locations the project developer will need to collect these data. For another example, a project near an urban area or near to a park may generate earlier and more substantive public concern than a site that is developed in mined lands. A calculation using the derivative from table 1 will show the different investment needs. Project cost will vary corresponding to data criticality and data availability.

42 ENGINEERING↗
DOE OSTI2021

Indexed improvements for real-time trotter evolution of a (1 + 1) field theory using NISQ quantum computers

Today's quantum computers offer the possibility of performing real-time calculations for quantum field theory scattering processes motivated by high energy physics. In order to follow the successful roadmap which has been established for the calculation of static properties at Euclidean time, it is crucial to develop new algorithmic methods to deal with the limitations of current noisy intermediate-scale quantum (NISQ) devices and to establish quantitative measures of the progress made with different devices. In this paper, we report recent progress in these directions. We show that nonlinear aspects of the trotter errors allow us to take much larger step then suggested by low-order analysis. This is crucial to reach physically relevant time scales with today's NISQ technology. We propose to use an index averaging absolute values of the difference between the accurately calculated trotter evolution of site occupations and their actual measurements on NISQ machines (G index) as a measure to compare results that have been obtained from different hardware platforms. Using the transverse Ising model in one spatial dimension with four sites we apply this metric across several hardware platforms. We study the results including readout mitigation and Richardson extrapolations and show that the mitigated measurements are very effective based on the analysis of the trotter step size modifications. Furthermore, we discuss how this advance in the trotter step size procedures can improve quantum computing physics scattering results and how this technical advance can be applied to other machines and noise mitigation methods.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

An Online Prototype Toolset for Predicting and Optimizing P&T Performance (FY23 Status Report)

A new web-based toolset is being developed to support ongoing remediation optimization efforts and implementation of an adaptive site management strategy for the 200 West Area Pump-and-Treat (P&T) system at the Hanford Site. This toolset, comprising the well performance index tool and the well optimization pre-screening tool, will offer a user-friendly interface to predict and optimize the P&T well network’s performance at a preliminary level. Efforts in fiscal year (FY) 2023 focused on three main components: updating the existing deep learning model for predicting P&T performance, designing and developing a prototype of a web-based performance index tool, and initiating the conceptual design of the well optimization pre-screening tool. The well performance index tool is based on a pre-trained deep learning model that allows users to select a target contaminant and well screen length, then visualize the predicted performance of potential new wells across the site. The well optimization pre-screening tool includes two separate modules: the pre-computed scenario viewer, which organizes and visualizes offline optimization simulation results, and the quick analysis module, which provides real-time model prediction using user-specified well locations. In FY24, the plan is to add web-based applications to SOCRATES for both the well performance prediction tool and the optimization prescreening tool, with accompanying user and theory guides. These tools are intended to enable an accessible, easily applied, and transparent approach to remedy planning and decision-making.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Public water supply infrastructure extensification and diversification in surface waters is insufficient to meet future demands in Texas

The data were developed to evaluate the capacity of existing and potential new surface water supply infrastructure to meet projected public water demands across districts in Texas under multiple future socioeconomic and climate scenarios. The database integrates hydrologic, water quality, infrastructure, energy, cost, demographic, and demand-projection information for candidate surface water supply locations. Candidate sites include stream reaches, waterbodies, reservoir surplus locations, and potential new reservoir sites. Water availability is characterized using historical and projected flow conditions, while site suitability is evaluated using five indicators: Water Availability Index (WAI), Water Quality Index (WQI), Energy Requirement Index (ERI), Water Treatment Cost (WTC), and Water Infrastructure Cost (WIC). The datasets include statewide candidate-site information, district-level demand projections under Shared Socioeconomic Pathways (SSPs), runoff-based allocation constraints, climate-stress metrics, and optimization outputs evaluating alternative infrastructure planning strategies. Optimization results compare Business-as-Usual (BAU) and All Surface Water (AllSW) demand-management approaches under both scaled and fixed cost-cap strategies. Associated validation datasets provide district-level feasibility assessments, infrastructure selection outcomes, cost-cap utilization, demand satisfaction metrics, and constraint diagnostics. Additional datasets quantify projected changes in storage and flow conditions as well as water availability stress for both existing and newly selected intake locations under the SSP5 scenario for mid-century and late-century climate conditions. Together, these datasets support assessment of the extent to which surface-water infrastructure expansion and diversification strategies can satisfy future public water demands while accounting for hydrologic, economic, and planning constraints across Texas. Dataset(s) Description Dataset_preoptimization.xlsx Comprehensive pre-optimization dataset containing candidate water-supply sites and associated hydrologic, water-quality, infrastructure, climate, demographic, runoff, and demand-projection variables used as inputs to the optimization analyses. Includes variable descriptions and the full statewide candidate-site database. District_level_site_selection.zip - Compressed archive containing all SSP-specific district-level optimization result files MESIO_ssp1_results.xlsx District-level site selection results for SSP1 (MESIO). Includes variable descriptions, BAU and AllSW site-selection results under scaled and fixed cost strategies, and district-level validation diagnostics. MESID_ssp2_results.xlsx District-level site selection results for SSP2 (MESID). Includes variable descriptions, BAU and AllSW site-selection results under scaled and fixed cost strategies, and district-level validation diagnostics. LCMRD_ssp3_results.xlsx District-level site selection results for SSP3 (LCMRD). Includes variable descriptions, BAU and AllSW site-selection results under scaled and fixed cost strategies, and district-level validation diagnostics. IRDev-Low_ssp4l_results.xlsx District-level site selection results for SSP4-Low (IRDev-Low). Includes variable descriptions, BAU and AllSW site-selection results under scaled and fixed cost strategies, and district-level validation diagnostics. IRDev-High_ssp4h_results.xlsx District-level site selection results for SSP4-High (IRDev-High). Includes variable descriptions, BAU and AllSW site-selection results under scaled and fixed cost strategies, and district-level validation diagnostics. RSIM_ssp5_results.xlsx District-level site selection results for SSP5 (RSIM). Includes variable descriptions, BAU and AllSW site-selection results under scaled and fixed cost strategies, and district-level validation diagnostics. tx_hydrological_stress.xlsx Hydrological stress dataset for existing and newly selected intake locations. Includes projected mid-century and late-century changes, gain/loss classifications, planning strategy information, and accompanying variable descriptions. Also includes water-stress metrics derived from historical and projected low-flow conditions.

Okoye, Perpetua I. (ORCID:0000000215545033)↗