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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Assimilation of Satellite-Based Snow Cover and Freeze/Thaw Observations Over the High Mountain Asia

Toward qualifying hydrologic changes in the High Mountain Asia (HMA) region, this study explores the use of a hyper-resolution (1 km) land data assimilation (DA)framework developed within the NASA Land Information System using the Noah Multi-parameterization Land Surface Model (Noah-MP) forced by the meteorological boundary conditions from Modern-Era Retrospective analysis for Research and Applications, Version 2 data. Two different sets of DA experiments are conducted:(1) the assimilation of a satellite-derived snow cover map (MOD10A1) and (2) the assimilation of the NASA MEaSUREs landscape freeze/thaw product from 2007 to 2008. The performance of the snow cover assimilation is evaluated via comparisons with available remote sensing-based snow water equivalent product and ground-based snow depth measurements. For example, in the comparison against ground-based snow depth measurements, the majority of the stations (13 of 14) show slightly improved goodness-of-fit statistics as a result of the snow DA, but only four are statistically significant. In addition, comparisons to the satellite-based land surface temperature products (MOD11A1 and MYD11A1) show that freeze/thaw DA yields improvements (at certain grid cells) of up to 0.58 K in the root-mean-square error (RMSE) and 0.77K in the absolute bias (relative to model-only simulations). In the comparison against three ground-based soil temperature measurements along the Himalayas, the bias and the RMSE in the 0-10 cm soil temperature are reduced (on average) by 10 and 7%,respectively. The improvements in the top layer of soil estimates also propagate through the deeper soil layers, where the bias and the RMSE in the 10-40 cm soil temperature are reduced (on average) by 9 and 6%, respectively. However, no statistically significant skill differences are observed for the freeze/thaw DA system in the comparisons against ground-based surface temperature measurements at mid-to-low altitude. Therefore, the two proposed DA schemes show the potential of improving the predictability of snow mass, surface temperature, and soil temperature states across HMA, but more ground-based measurements are still required, especially at high-altitudes, in order to document a more statistically significant improvement as a result of the two DA schemes.

High Mountain Asia↗
DOE OSTI2023

A semi-agnostic ansatz with variable structure for variational quantum algorithms

Quantum machine learning—and specifically Variational Quantum Algorithms (VQAs)—offers a powerful, flexible paradigm for programming near-term quantum computers, with applications in chemistry, metrology, materials science, data science, and mathematics. Here, one trains an ansatz, in the form of a parameterized quantum circuit, to accomplish a task of interest. However, challenges have recently emerged suggesting that deep ansatzes are difficult to train, due to flat training landscapes caused by randomness or by hardware noise. This motivates our work, where we present a variable structure approach to build ansatzes for VQAs. Our approach, called VAns (Variable Ansatz), applies a set of rules to both grow and (crucially) remove quantum gates in an informed manner during the optimization. Consequently, VAns is ideally suited to mitigate trainability and noise-related issues by keeping the ansatz shallow. We employ VAns in the variational quantum eigensolver for condensed matter and quantum chemistry applications, in the quantum autoencoder for data compression and in unitary compilation problems showing successful results in all cases.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Graph neural networks for CO 2 solubility predictions in Deep Eutectic Solvents

Deep Eutectic Solvents (DESs) are a promising class of solvents for CO 2 capture. DESs are complex mixtures that can be designed to optimize CO solubility and overall capture process efficiency. However, the vast design landscape of DES mixtures makes experimental investigation prohibitive; as such, there is a need for computational models that can quickly and efficiently navigate the design space and inform data collection efforts. In this work, we propose Graph Neural Network (GNN) models for predicting CO 2 solubility for DESs; the GNN leverages a mixture graph representation that captures the molecular structure of the DES components as well as their intermolecular interactions. Here, we compare the GNN framework against alternative architectures (neural networks, graph convolution networks, and random forests) and data representations (molecular fingerprints, sigma profiles, and graphs). We show that the proposed approach offers superior predictive performance; specifically, we show that solubility can be predicted reliably directly from molecular structure (without the need of using sigma profiles as proposed in previous studies). This result is important, as obtaining sigma profiles requires expensive density functional theory computations. We also explored the ability of GNNs to predict solubility for new DES mixtures and operating conditions. We found that the model extrapolates across temperature reliably. However, we also found deficiencies in the ability of the model to predict solubility for DES mixtures, pressures, and molar ratio not included in the training sets; we show that this is due to an inherent lack of chemical diversity in datasets available in the literature. The proposed computational capabilities can thus help navigate the design space of DES and inform data collection efforts. Our models, data, and benchmarks are shared as Python code implemented in Jupyter notebooks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Characterizing and mitigating coherent errors in a trapped ion quantum processor using hidden inverses

Quantum computing testbeds exhibit high-fidelity quantum control over small collections of qubits, enabling performance of precise, repeatable operations followed by measurements. Currently, these noisy intermediate-scale devices can support a sufficient number of sequential operations prior to decoherence such that near term algorithms can be performed with proximate accuracy (like chemical accuracy for quantum chemistry problems). While the results of these algorithms are imperfect, these imperfections can help bootstrap quantum computer testbed development. Demonstrations of these algorithms over the past few years, coupled with the idea that imperfect algorithm performance can be caused by several dominant noise sources in the quantum processor, which can be measured and calibrated during algorithm execution or in post-processing, has led to the use of noise mitigation to improve typical computational results. Conversely, benchmark algorithms coupled with noise mitigation can help diagnose the nature of the noise, whether systematic or purely random. Here, we outline the use of coherent noise mitigation techniques as a characterization tool in trapped-ion testbeds. We perform model-fitting of the noisy data to determine the noise source based on realistic physics focused noise models and demonstrate that systematic noise amplification coupled with error mitigation schemes provides useful data for noise model deduction. Further, in order to connect lower level noise model details with application specific performance of near term algorithms, we experimentally construct the loss landscape of a variational algorithm under various injected noise sources coupled with error mitigation techniques. This type of connection enables application-aware hardware codesign, in which the most important noise sources in specific applications, like quantum chemistry, become foci of improvement in subsequent hardware generations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1971

Application of Remote Sensing Techniques for Appraising Changes in Wildlife Habitat

An attempt was made to investigate the potential of airborne, multispectral, line scanner data acquisition and computer-implemented automatic recognition techniques for providing useful information about waterfowl breeding habitat in North Dakota. The spectral characteristics of the components of a landscape containing waterfowl habitat can be detected with airborne scanners. By analyzing these spectral characteristics it is possible to identify and map the landscape components through analog and digital processing methods. At the present stage of development multispectral remote sensing techniques are not ready for operational application to surveys of migratory bird habitat and other such resources. Further developments are needed to: (1) increase accuracy; (2) decrease retrieval and processing time; and (3) reduce costs.

Nelson, H. K.↗
NASA NTRS1997

Evaluation of GIS Technology in Assessing and Modeling Land Management Practices

There is an increasing concern of land owners to protect and maintain healthy and sustainable agroecosystems through the implementation of best management practices (BMP). The objectives of this study were: (1) To develop and evaluate the use of a Geographic Information System (GIS) technology for enhancing field-scale management practices; (2) evaluate the use of 2-dimensional displays of the landscape and (3) define spatial classes of variables from interpretation of geostatistical parameters. Soil samples were collected to a depth of 2 m at 15 cm increments. Existing data from topographic, land use, and soil survey maps of the Winfred Thomas Agricultural Research Station were converted to digital format. Additional soils data which included texture, pH, and organic matter were also generated. The digitized parameters were used to create a multilayered field-scale GIS. Two dimensional (2-D) displays of the parameters were generated using the ARC/INFO software. The spatial distribution of the parameters evaluated in both fields were similar which could be attributed to the similarity in vegetation and surface elevation. The ratio of the nugget to total semivariance, expressed as a percentage, was used to assess the degree of spatial variability. The results indicated that most of the parameters were moderate spatially dependent Biophysical constraint maps were generated from the database layers, and used in multiple combination to visualize results of the BMP. Understanding the spatial relationships of physical and chemical parameters that exists within a field should enable land managers to more effectively implement BMP to ensure a safe and sustainable environment.

Archer, F.↗
NASA NTRS2007

Analysis of The Surface Radiative Budget Using ATLAS Data for San Juan, Puerto Rico

The additional beating of the air over the city is the result of the replacement of naturally vegetated surfaces with those composed of asphalt, concrete, rooftops and other man-made materials. The temperatures of these artificial surfaces can be 20 to 40 C higher than vegetated surfaces. This produces a dome of elevated air temperatures 5 to 8 C greater over the city, compared to the air temperatures over adjacent rural areas. Urban landscapes are a complex mixture of vegetated and nonvegetated surfaces. It is difficult to take enough temperature measurements over a large city area to characterize the complexity of urban radiant surface temperature variability. The NASA Airborne Thermal and Land Applications Sensor (ATLAS) operates in the visual and IR bands was used in February 2004 to collect data from San Juan, Puerto Rico with the main objective of investigating the Urban Heat Island (UHI) in tropical cities.

Luvall, Jeffrey C.↗
NASA NTRS2017

Imaging of Earthquake Faults Using Small UAVs as a Pathfinder for Air and Space Observations

Large earthquakes cause billions of dollars in damage and extensive loss of life and property. Geodetic and topographic imaging provide measurements of transient and long-term crustal deformation needed to monitor fault zones and understand earthquakes. Earthquake-induced strain and rupture characteristics are expressed in topographic features imprinted on the landscapes of fault zones. Small UAVs provide an efficient and flexible means to collect multi-angle imagery to reconstruct fine scale fault zone topography and provide surrogate data to determine requirements for and to simulate future platforms for air- and space-based multi-angle imaging.

Donnellan, Andrea↗
NASA NTRSDate not supplied

NASA POWER: Providing Analysis-Ready, Cloud-Optimized Data for AI /ML Training and Applications in Earth Science

As global demand for sustainable development grows, the integration of Earth Observation (EO) data into decision making frameworks has become a primary objective for the scientific community. The NASA Prediction of Worldwide Energy Resources (POWER) project serves as a bridge between NASA EO data and the specialized needs of the renewable energy, sustainable infrastructure and agroclimatology communities. In this poster presentation we will present an overview of POWER data products and services along with its use in diverse research to decision-making workflows. By providing over 40 years of high-resolution historical, hourly and daily solar and meteorological data, POWER transforms satellite observations and global model reanalysis into actionable, Analysis-Ready Dataset (ARD). Currently, the project delivers over 250 industry-friendly parameters to the users from different NASA datasets like CERES SYN1Deg, MERRA-2, and IMERG alongside downscaled CMIP6 climate model data, fulfilling over 16 million requests from 50,000 unique users monthly. To ensure data quality and traceability, these parameters are rigorously validated against the ground-based observations from the Baseline Surface Radiation Network (BSRN) and the Global Surface Summary of the Day (GSOD) – these results will be discussed in the presentation. A newly introduced web-based PaRameter Uncertainty ViEwer (PRUVE) tool will be presented that provides an online validation platform to the users that benchmarks satellite-based and assimilation data products against these surface measurements. To reduce technical barriers to data adoption, POWER data is accessible through RESTful APIs, ESRI ArcGIS Image Services, a web-based Data Access Viewer tool, allowing users to visualize, validate and apply the dataset. For efficient data delivery POWER data is cloud-optimized into Zarr datastore accessible through NASA managed Amazon S3 ensures high-performance allowing users to integrate EO directly into operational pipelines. These customized services will be presented. Use cases from application will be presented from the energy sector - such as for design of generation systems, performance monitoring of solar power plants, in infrastructure sector- optimizing building energy efficiency and thermal comfort, in agriculture – such as driving crop simulation and yield forecasting models to enable climate resilient farming. Furthermore, the shift toward machine learning (ML) in EO research that has positioned POWER as a key provider for training datasets which will be discussed. Use-cases will be presented to showcase how NASA data is enabling the development of predictive tools for climate variability and resource management. The poster will present POWER’s future plans including technology development to enhance data traceability and reproducibility and improving I/O performance to support the rapid integration of new EO products, ensuring that POWER remains a robust scalable backend for the evolving landscape of AI-driven Earth Science. Additionally, POWER is developing an AI Agent and an MCP-Server to enable industry AI-Agentic workflows.

Neha Khadka↗
DOE OSTI2021

A bird of two hemispheres: an examination of Swainson’s hawk (Buteo swainsoni) ecology across a landscape of increasing wind energy development

The purpose of this study was to expand knowledge on the life history of Swainson’s hawks (Buteo swainsoni), including aspects of breeding, juvenile periods, migration, and risks posed by the wind energy industry. Data presented in this dissertation was collected in northern Texas (Armstrong, Carson, Hale, Hockley, Lubbock, Potter, Randall, and Swisher counties) from 2012 to 2018 and from satellite transmitters attached to hawks from 2012 to 2021. Information has been formatted to facilitate future publication of results and is presented here in discrete chapters grouping similar categories of information; some redundancy exists among chapters, but the bulk of each chapter contains unique analyses, results, and conclusions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Storing Affordability: Battery Storage as an Asset to Reduce Data Center Cost Shifts

This report examines how battery energy storage systems (BESS) can help utilities accommodate large load growth while protecting affordability for existing ratepayers. Rapid growth in electricity demand from artificial intelligence (AI) data centers is straining the U.S. grid. Furthermore, many new data centers are entering rural markets, which could offer economic benefits but may also pose implementation challenges for smaller utilities. At the same time, retail electricity prices are increasing faster than inflation, elevating customer affordability as a key challenge. While data centers have not been the primary driver of increases in residential prices to date, they have pushed wholesale energy and capacity prices higher in several markets. Fundamental utility cost-allocation principles show that data center growth can be rate-positive for existing customers only if new peak demand grows faster than the costs a utility must incur to serve it. Several factors, including a utility’s degree of wholesale market exposure, forecast uncertainty and stranded-asset risk, and tariff design can determine the outcome of load growth on retail rates. Energy storage can make several affordability contributions in the face of this landscape of uncertainty and market volatility, including deferral of higher-cost grid investments through improved utilization of existing assets and flexibility of new large loads, insulation from volatile wholesale prices through peak shaving, and reliability support to address grid risks stemming from the behavior of AI data center loads. Different potential BESS deployment pathways—utility-scale front-of-the-meter systems, aggregated small-scale storage installations, and data center-sited behind-the-meter storage—are compared against each other and against conventional capacity alternatives. This framework is intended as a conceptual resource to utilities, particularly smaller public utilities with rural service territories, who may be considering the role that energy storage can play in insulating existing ratepayers from data center cost shifts.

25 ENERGY STORAGE↗
DOE OSTI2026

Multi-resolution Arctic Shrub Cover Dataset Derived from UAS and Airborne SfM and LiDAR (2013-2025)

We synthesized 177 unoccupied aerial system flights and 77 airborne flights across the Arctic and created a multi-resolution benchmark data of low-to-tall shrub fractional cover leveraging Structure-from-Motion and Light Detection and Ranging. The resulting dataset covered a total of 1899 km2 across Alaska, Western Canada, Sweden, and Siberian Arctic, including key sites from the Oro Arctic to the High Arctic. The dataset is organized into 6 primary data collection directories (“Abisko,” “AWI,” “ERE,” “Fairbanks,” “NGEE,” “Toolik”), each containing site and flight subdirectories. Flight directories include shrub cover rasters (*.tifs) at 1 m, 5 m, and 30 m resolution, the canopy height model at 1 m resolution (*.tifs), and a bounding box *.kml file. For the AWI, Abisko, NGEE, and Fairbanks collections, we also include the GCC raster at 1 m resolution (*.tif). Files are organized by Collection > Site > Flight Name > Data Files. Flight rasters are in the local UTM zone and the .kml files are in the geographic coordinate system EPSG 4326. We also include a .csv file that details the source datasets for every flight. The Next-Generation Ecosystem Experiments in the Arctic (NGEE Arctic) project is a research effort to reduce uncertainty in the Department of Energy’s Energy Exascale Earth System Model (E3SM) by developing a predictive understanding of Arctic tundra ecosystems underlain by permafrost and to quantify feedbacks from the Arctic tundra to the Earth system. NGEE Arctic is supported by the Department of Energy's Office of Biological and Environmental Research. Over Phases 1–3, observations made by the NGEE Arctic team across a gradient of permafrost landscapes in Arctic Alaska improved the representation of tundra processes in the land surface component of E3SM (the E3SM Land Model, ELM). Model improvements emphasized unique aspects of permafrost environments and explored reductions in model complexity while retaining predictive power. The Arctic-informed ELM developed by NGEE Arctic has been used to make novel predictions on processes ranging from permafrost thaw to soil biogeochemical cycling to Earth system feedbacks associated with the unique characteristics of tundra plants. In Phase 4, the NGEE Arctic team is evaluating our new predictive understanding under novel conditions across the Arctic domain. In collaboration with partners at long-term pan-Arctic research sites we are examining whether an Arctic-informed ELM can faithfully simulate interactions among surface and subsurface processes at site, regional, and pan-Arctic scales. In turn, we are using variety of tools to dynamically extend and evaluate ELM inference, with an emphasis on data synthesis and pan-Arctic model evaluation, reintegration of code with an evolving E3SM, scaling across heterogeneous Arctic landscapes, and the appropriate representation of the impacts of increasingly frequent Arctic disturbances.

canopy height model↗
DOE OSTI2022

Orthoimagery and Shapefiles Documenting Pre- and Post-August 2019 Slope Disturbances, Teller Road Site, Seward Peninsula, Alaska, 2018-2019

This dataset was derived from UAS aerial photos and dGPS data collected at the Teller mile marker 47 site in 2018 and 2019 and used to support research quantifying the timing and rate of surface movements and analyze soil transport process in Arctic landscapes. In July 2018, a Phantom uncrewed aerial system (UAS) collected >6800 high resolution aerial photos of the lower portion of the Teller 47 watershed (see for raw photos; pending archive NGA281). During the UAS survey, 25 x 25 cm tile ground control points (GCPs) were laid out and secured with one rebar rod in the center. The four corners and rebar tops were surveyed with differential GPS, and these coordinates were used to construct and validate an orthomosaic of the site. These points were resurveyed in August 2019 and used to track annual movement. Changes in position between the two surveys are reported in (Lathrop et al. 2022; NGA254). Agisoft Metashape photogrammetry software was used to construct a georeferenced orthomosaic image (*.tif file) of a portion (0.68 km2) of the watershed with a final resolution of ~1 cm. Manual delineation of the perimeters of failures visible in the UAS imagery (*.tif files) was conducted to create shapefile polygons (two *.zip files). The failures were identified by the exposure of bare mineral soils, which were made visible by disruption of the overlying tundra vegetation. The shapefiles were then used to analyse the topographic distribution and sizes of the failures. In August 2019 an additional ~300 georeferenced UAS images of slope instability features were collected (data pending submission) and compared with the 2018 orthomosaic. Overview maps of the failure locations included as a *.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 OSTI2021

Systems and methods for improved landscape management

Disclosed here are systems, methods, apparatus, and/or non-transitory computer-readable storage comprising machine-readable code for the development and application of high-resolution crop yield models. The disclosed yield models may be captured yield data and corresponding remote sensing data covering relatively limited areas. Embodiments of the disclosed yield models may be capable of estimating spatial yield characteristics in areas for which accurate yield data are not available (and/or not practical to acquire), thereby enabling more widespread application of integrated land management techniques.

Griffel, Lloyd M.↗
DOE OSTI2020

Augmenting machine learning of energy landscapes with local structural information

We present a machine learning approach for accurately predicting formation energies of binary compounds in the context of crystal structure predictions. The success of any machine learning model depends significantly on the choice of representation used to encode the relevant physical information into machine-learnable data. We test different representation schemes based on partial radial and angular distribution functions (RDF+ADF) on Al–Ni and Cd–Te structures generated using our genetic algorithm for structure prediction. We observe a remarkable improvement in predictive accuracy upon transitioning from global to atom-centered representations, resulting in a threefold decrease in prediction errors. We show that a support vector regression model using a combination of atomic radial and angular distribution functions performs best at the formation energy prediction task, providing small root mean squared errors of 3.9 meV/atom and 10.9 meV/atom for Al–Ni and Cd–Te, respectively. We test the performance of our models against common traditional descriptors and find that RDF- and ADF-based representations significantly outperform many of those in the prediction of formation energies. The high accuracy of predictions makes our machine learning models great candidates for the exploration of energy landscapes.

Honrao, Shreyas J. (ORCID:0000000166155257)↗
DOE OSTI2021

Identifying Key Environmental Factors Explaining Temporal Patterns of DOC Export From Watersheds in the Conterminous United States

The export of dissolved organic carbon (DOC) from a watershed is a key component of the terrestrial biosphere carbon cycle. There is a need to improve our understanding of how and by how much various environmental factors are driving the temporal patterns of DOC export in order to accurately model and evaluate terrestrial carbon storage and fluxes. In this synthesis, we compiled observational data sets from 14 watersheds in the conterminous United States spanning the time period from 1981 to 2017. We used these data sets to examine the relative impacts of various climate, atmospheric deposition, and land cover factors on the temporal patterns of DOC export across watersheds of different sizes and landscape conditions, as well as the time-series autocorrelation of DOC export. Overall, our results suggest that the dominant factor on an annual scale was the amount of precipitation, which had a positive correlation with DOC export from a watershed. Overall increasing nitrogen deposition was coincident with increasing DOC export, and increasing sulfur deposition was coincident with declining DOC export. The seasonal pattern of DOC export was strongly regulated by air temperature, the long-term trend was negatively influenced by increasing sulfur deposition, and no obvious autocorrelation was detected in DOC export. In addition, higher rates of DOC export were positively correlated with greater area of wetlands within a watershed, but was not found to be strongly related to any of the other land cover types.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Hurricane-Induced Rainfall is a Stronger Predictor of Tropical Forest Damage in Puerto Rico Than Maximum Wind Speeds

Projected increases in cyclonic storm intensity under a warming climate will have profound effects on forests, potentially changing these ecosystems from carbon sinks to sources. Forecasting storm impacts on these ecosystems requires consideration of risk factors associated with storm meteorology, landscape structure, and forest attributes. Here we evaluate risk factors associated with damage severity caused by Hurricanes María and Irma across Puerto Rican forests. Using field and remote sensing data, total forest aboveground biomass (AGB) lost to the storms was estimated at 10.44 (±2.33) Tg, ca. 23% of island-wide pre-hurricane forest AGB. Storm-related rainfall was a stronger predictor of forest damage than maximum wind speeds. Soil water storage capacity was also an important risk factor, corroborating the influence of rainfall on forest damage. Expected increases of 20% in hurricane-associated rainfall in the North Atlantic highlight the need to consider how such shifts, together with high speed winds, will affect terrestrial ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Geographic Information System Mapping Tool for Rainwater Harvesting in the United States

The Rainwater Harvesting Tool is a publicly available web-based geographic information system tool developed using geospatial analysis in combination with historic ZIP Code level monthly average precipitation and evapotranspiration data across the United States to help select potential locations for harvesting rainwater. Rainwater harvesting can provide a key source of alternative water for a variety of uses including landscape irrigation, vehicle wash, cooling tower makeup, dust suppression, and toilet flushing. Rainwater harvesting can help support institutional, commercial, and residential buildings in diversifying water sources and offset the use of freshwater. This tool aims to help organizations strategically target locations to implement rainwater harvesting systems. The metric used in the tool is called the rainwater harvesting potential, which is a normalized metric, measured in inches per year. The rainwater harvesting potential describes the amount of rainwater that can be reasonably collected and stored at a specific location. This metric was used to rank areas delineated by ZIP Codes across the US, from lowest to highest to show the relative availability of rainwater for harvesting. Two mapping layers are included in the tool that show the general rainwater harvesting potential for all applications and a layer that specifically shows the potential for harvesting rainwater to supply irrigation The Rainwater Harvesting Tool allows users to view overall trends across the United States, while also allowing the user to zoom in to a scale where ZIP Code boundaries are clearly delineated. The tool can be used to help organizations with buildings located in multiple regions to strategically identify where to install rainwater harvesting systems and prioritize locations that may be optimal for rainwater harvesting.

47 OTHER INSTRUMENTATION↗