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At least 1,207 records · Page 67

All sources NASA NTRS DOE OSTI

NASA NTRS2005

Improving Situational Awareness for First Responders via Mobile Computing

This project looks to improve first responder situational awareness using tools and techniques of mobile computing. The prototype system combines wireless communication, real-time location determination, digital imaging, and three-dimensional graphics. Responder locations are tracked in an outdoor environment via GPS and uploaded to a central server via GPRS or an 802.11 network. Responders can also wirelessly share digital images and text reports, both with other responders and with the incident commander. A pre-built three dimensional graphics model of a particular emergency scene is used to visualize responder and report locations. Responders have a choice of information end points, ranging from programmable cellular phones to tablet computers. The system also employs location-aware computing to make responders aware of particular hazards as they approach them. The prototype was developed in conjunction with the NASA Ames Disaster Assistance and Rescue Team and has undergone field testing during responder exercise at NASA Ames.

Betts, Bradley J.↗

DOE OSTI2021

Ride-hailing and taxi versus walking: Long term forecasts and implications from large-scale behavioral data

Introduction: Although ride-hailing and taxi trips can potentially reduce single-occupant vehicle trips and auto ownership, they can also replace pedestrian trips. Because physical activity is associated with improved health outcomes, the extent to which ride-hailing and taxi travel captures walking's mode share is of interest to policymakers. Methods: Based on large-scale behavioral data from the 2017 U.S. National Household Travel Survey, this paper reports on the development of a full Bayesian logistic regression model for determining the mode split between (1) ride-hailing and taxi and (2) walk while accounting for unobserved heterogeneity. The results from the stand-alone model inform two longer-term travel forecasting scenarios: a) higher risk of walk trips converting to ride-hailing and taxi, specifically in the future with high prevalence of automated vehicles, b) higher probability of such trips remaining as walking. Results: The results revealed that some of the important characteristics that increase the likelihood of a traveler using the ride-hailing and taxi mode versus walking include having a longer trip, using a smartphone to access the internet, having an interest in technologies, having a medical condition, and living in a metropolitan area with rail access. Further, the results from the first scenario suggest that an overall increase of up to 2.9% in the ride-hailing and taxi mode share may be expected. The second scenario shows that between 68% and 76% of ride-hailing and taxi trips could be diverted to walking if supportive pedestrian infrastructure were provided in the case study locations. The planning process can be adapted to consider not only congestion, crash, and emissions impacts of such shifts but also the effects of a loss of physical activity. Conclusions: The study findings show how the ride-hailing and taxi mode competes with walking. Further, the findings enable planners to update their regional travel forecasting models; policy makers can thus encourage active travel by prioritizing pedestrian infrastructure investments that may divert ride-hailing and taxi trips to walking. However, equity should be a key consideration to ensure that addressing the competition between these two modal choices does not hinder the provision of pedestrian facilities in communities that depend on walking.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2020

The Large-Eddy Simulation (LES) Atmospheric Radiation Measurement (ARM) Symbiotic Simulation and Observation (LASSO) Activity for Continental Shallow Convection

The U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) user facility recently initiated the Large-Eddy Simulation (LES) ARM Symbiotic Simulation and Observation (LASSO) activity focused on shallow convection at ARM’s Southern Great Plains (SGP) atmospheric observatory in Oklahoma. LASSO is designed to overcome an oft-shared difficulty of bridging the gap from point-based measurements to scales relevant for model parameterization development, and it provides an approach to add value to observations through modeling. LASSO is envisioned to be useful to modelers, theoreticians, and observationalists needing information relevant to cloud processes. LASSO does so by combining a suite of observations, LES inputs and outputs, diagnostics, and skill scores into data bundles that are freely available, and by simplifying user access to the data to speed scientific inquiry. The combination of relevant observations with observationally constrained LES output provides detail that gives context to the observations by showing physically consistent connections between processes based on the simulated state. A unique approach for LASSO is the generation of a library of cases for days with shallow convection combined with an ensemble of LES for each case. The library enables researchers to move beyond the single-case-study approach typical of LES research. The ensemble members are produced using a selection of different large-scale forcing sources and spatial scales. Since large-scale forcing is one of the most uncertain aspects of generating the LES, the ensemble informs users about potential uncertainty for each date and increases the probability of having an accurate forcing for each case.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

A Collaboration Website for Muon Catalyzed Fusion and Muon Beam Production

This project sets up a website to support the nascent Muon Catalyzed Fusion collaboration including development of particle accelerators and transport beamlines for muon beams. The website is envisioned as having the general public information pages and private pages for collaboration members. Multimedia elements like images, text animations, and video lectures, covering a broad spectrum of topics will populate the educational site, covering muon facilities, to comprehensive explorations and seminal documents that define the science of Muon Catalyzed Fusion, Acceleration, Applications, Instrumentation, Beamline Design, and beam dynamics design codes. Ensuring compatibility across devices and operating systems, it also features integration with Google Docs for collaboration, a code repository (GitHub), a blog platform with comments (WordPress), the potential for ChatGPT integration and interactive graph plotting with Python Plotty to enhance data visualization. This project will maintain public and protected private pages, due to the proprietary nature of the work or research in progress. The public sections will be built to foster dissemination of information and highlight recent work within the NK Labs collaboration, including lectures, published papers, and regular blog posts with open commenting. The private section will support unpublished or nonpublic research, by facilitating collaborative efforts through integrated Google Docs and Python Plotty for shared graphing work. Ultimately, this project strives to make complex scientific knowledge more accessible to the public, foster enhanced collaboration, and serve as a platform for sharing cutting-edge research in Muon Catalyzed Fusion and Accelerators.

43 PARTICLE ACCELERATORS↗

DOE OSTI2025

An evolving Coupled Model Intercomparison Project phase 7 (CMIP7) and Fast Track in support of future climate assessment

The Coupled Model Intercomparison Project (CMIP) coordinates community-based efforts to answer key and timely climate science questions, facilitate delivery of relevant multi-model simulations through shared infrastructure, and support national and international climate assessments. Generations of CMIP have evolved through extensive community engagement from punctuated phasing into more continuous support for the design of experimental protocols, infrastructure for data publication and access, and public delivery of climate information. We identify four fundamental research questions motivating a seventh phase of coupled model intercomparison relating to patterns of sea surface temperature change, changing weather, the water–carbon–climate nexus, and tipping points. Key CMIP7 advances include an expansion of baseline experiments, a focus on CO 2 -emissions-driven experiments, sustained support for community MIPs, periodic updating of historical forcings and diagnostics requests, and a collection of prioritized experiments, or the “Assessment Fast Track”, drawn from community MIPs to support climate research, assessment, and service goals across prediction and projection, characterization, attribution, and process understanding.

Environmental sciences↗

DOE OSTI2024

Sharing the Sun Community Solar Project Data

This database represents a list of community solar projects, complete and pending, identified through various sources. The dataset is updated multiple times per year. The current version is the first file located below. Previous versions of the dataset published before June of 2024 can be found in the dataset below labeled “ARCHIVE_Sharing the Sun Community Solar Project Data_Before 06.24.“ The list has been reviewed but errors may exist, and the list may not be comprehensive. Errors in the sources e.g. press releases may be duplicated in the list. Blank spaces represent missing information. NLR invites input to improve the database including, to correct erroneous information, add missing projects, fill in missing information, and remove inactive projects. Updated information can be submitted to Sudha Kannan ( sudha.kannan@nlr.gov ).

14 SOLAR ENERGY↗

NASA NTRS1980

Microprocessor user support at Langley Research Center

The use of microprocessors pose significant problems including: (1) a long learning process for proficient use of microprocessors; (2) the requirement for extensive support in both hardware and software; and (3) the need for coordination and sharing of the creative effort to avoid unnecessary duplication. To address these problems, Langley Research Center has established a microprocessor users committee to provide an advisory interface for management and users, and is training microprocessor users. A newsletter is published to disseminate information among microprocessor users. Both cross software on the central computer complex and microprocessor development systems are used to support the design of microprocessor based systems. Each of these activities is reviewed with special emphasis given to the microprocessor support available from the central computer complex. The effectiveness of the approach being taken at Langley is assessed and specific hardware and software development efforts that are targeted toward enhancing the existing microprocessing support are discussed.

Tucker, J. H.↗

NASA NTRS2007

Automating CapCom Using Mobile Agents and Robotic Assistants

Mobile Agents (MA) is an advanced Extra-Vehicular Activity (EVA) communications and computing system to increase astronaut self-reliance and safety, reducing dependence on continuous monitoring and advising from mission control on Earth. MA is voice controlled and provides information verbally to the astronauts through programs called "personal agents." The system partly automates the role of CapCom in Apollo-including monitoring and managing navigation, scheduling, equipment deployment, telemetry, health tracking, and scientific data collection. Data are stored automatically in a shared database in the habitat/vehicle and mirrored to a site accessible by a remote science team. The program has been developed iteratively in authentic work contexts, including six years of ethnographic observation of field geology. Analog field experiments in Utah enabled empirically discovering requirements and testing alternative technologies and protocols. We report on the 2004 system configuration, experiments, and results, in which an EVA robotic assistant (ERA) followed geologists approximately 150 m through a winding, narrow canyon. On voice command, the ERA took photographs and panoramas and was directed to serve as a relay on the wireless network.

Clancey, William J.↗

NASA NTRS2009

Planetary Geologic Mapping Handbook - 2009

Geologic maps present, in an historical context, fundamental syntheses of interpretations of the materials, landforms, structures, and processes that characterize planetary surfaces and shallow subsurfaces (e.g., Varnes, 1974). Such maps also provide a contextual framework for summarizing and evaluating thematic research for a given region or body. In planetary exploration, for example, geologic maps are used for specialized investigations such as targeting regions of interest for data collection and for characterizing sites for landed missions. Whereas most modern terrestrial geologic maps are constructed from regional views provided by remote sensing data and supplemented in detail by field-based observations and measurements, planetary maps have been largely based on analyses of orbital photography. For planetary bodies in particular, geologic maps commonly represent a snapshot of a surface, because they are based on available information at a time when new data are still being acquired. Thus the field of planetary geologic mapping has been evolving rapidly to embrace the use of new data and modern technology and to accommodate the growing needs of planetary exploration. Planetary geologic maps have been published by the U.S. Geological Survey (USGS) since 1962 (Hackman, 1962). Over this time, numerous maps of several planetary bodies have been prepared at a variety of scales and projections using the best available image and topographic bases. Early geologic map bases commonly consisted of hand-mosaicked photographs or airbrushed shaded-relief views and geologic linework was manually drafted using mylar bases and ink drafting pens. Map publishing required a tedious process of scribing, color peel-coat preparation, typesetting, and photo-laboratory work. Beginning in the 1990s, inexpensive computing, display capability and user-friendly illustration software allowed maps to be drawn using digital tools rather than pen and ink, and mylar bases became obsolete. Terrestrial geologic maps published by the USGS now are primarily digital products using geographic information system (GIS) software and file formats. GIS mapping tools permit easy spatial comparison, generation, importation, manipulation, and analysis of multiple raster image, gridded, and vector data sets. GIS software has also permitted the development of project-specific tools and the sharing of geospatial products among researchers. GIS approaches are now being used in planetary geologic mapping as well (e.g., Hare and others, 2009). Guidelines or handbooks on techniques in planetary geologic mapping have been developed periodically (e.g., Wilhelms, 1972, 1990; Tanaka and others, 1994). As records of the heritage of mapping methods and data, these remain extremely useful guides. However, many of the fundamental aspects of earlier mapping handbooks have evolved significantly, and a comprehensive review of currently accepted mapping methodologies is now warranted. As documented in this handbook, such a review incorporates additional guidelines developed in recent years for planetary geologic mapping by the NASA Planetary Geology and Geophysics (PGG) Program s Planetary Cartography and Geologic Mapping Working Group s (PCGMWG) Geologic Mapping Subcommittee (GEMS) on the selection and use of map bases as well as map preparation, review, publication, and distribution. In light of the current boom in planetary exploration and the ongoing rapid evolution of available data for planetary mapping, this handbook is especially timely.

Tanaka, K. L.↗

NASA NTRS2010

Benefits of Using Remote Sensing for Health Alerts and Chronic Respiratory Exposures

Respiratory diseases such as asthma can be triggered by environmental conditions that can be monitored using Earth observing data and environmental forecast models. Frequent dust storms in the southwestern United States, the annual cycle of juniper pollen events in the spring, and increased aerosol and ozone concentrations in summer, are health concerns shared by the community at large. Being able to forecast the occurrence of these events would help the health care community prepare for increased visits to emergency rooms, as well as allow public health officials to issue alerts to affected persons. This information also is important to epidemiologists for analyzing long-term trends and impacts of these events on the health and well-being of the community. Earth observing data collected by remote sensing platforms are important for improving the performance of models that can forecast these events, and in turn, improve products and information for decision-making by public health authorities. This presentation will discuss the benefits of using remote sensing data for forecasting environmental events that can adversely affect individuals with respiratory ailments. The presentations will include a brief discussion on relevant Earth observing data, the forecast models used, and societal benefits of the resulting products and information. Several NASA-funded projects will be highlighted as examples

Luvall, J. C.↗

NASA NTRS2017

Understanding the International Space Station Crew Perspective following Long-Duration Missions through Data Analytics & Visualization of Crew Feedback

The International Space Station (ISS) first became a home and research laboratory for NASA and International Partner crewmembers over 16 years ago. Each ISS mission lasts approximately 6 months and consists of three to six crewmembers. After returning to Earth, most crewmembers participate in an extensive series of 30+ debriefs intended to further understand life onboard ISS and allow crews to reflect on their experiences. Examples of debrief data collected include ISS crew feedback about sleep, dining, payload science, scheduling and time planning, health & safety, and maintenance. The Flight Crew Integration (FCI) Operational Habitability (OpsHab) team, based at Johnson Space Center (JSC), is a small group of Human Factors engineers and one stenographer that has worked collaboratively with the NASA Astronaut office and ISS Program to collect, maintain, disseminate and analyze this data. The database provides an exceptional and unique resource for understanding the "crew perspective" on long duration space missions. Data is formatted and categorized to allow for ease of search, reporting, and ultimately trending, in order to understand lessons learned, recurring issues and efficiencies gained over time. Recently, the FCI OpsHab team began collaborating with the NASA JSC Knowledge Management team to provide analytical analysis and visualization of these over 75,000 crew comments in order to better ascertain the crew's perspective on long duration spaceflight and gain insight on changes over time. In this initial phase of study, a text mining framework was used to cluster similar comments and develop measures of similarity useful for identifying relevant topics affecting crew health or performance, locating similar comments when a particular issue or item of operational interest is identified, and providing search capabilities to identify information pertinent to future spaceflight systems and processes for things like procedure development and training. In addition, the comments were scored for sentiment using a polarity scoring algorithm to identify both positive and negative comments for particular groups and clusters, allowing the team to make analytically informed decisions regarding future hardware and operating procedures. The use of polarity scoring with time series analysis was used to provide insight into how crew health and habitability is changing throughout various spaceflight increments or the station lifecycle as a whole. Finally, a visualization framework was developed to address the needs of the end users to search for and analyze comments by user, category or mission. This paper will discuss how the use of an analytical framework in conjunction with the current human interface, improved the understanding of crew perspective and shortened the time for analysis allowing for more informed decisions and rapid development of improvements. These methods are significantly optimizing the way that this valuable data can be assessed and applied to current and future spaceflight design and development. This collaboration allows the FCI OpsHab team to effectively analyze and share data in a more automated and timely fashion. Trends are no longer derived manually and can be illustrated effectively and accurately with these evolving techniques to an ever growing group of human spaceflight end users.

Bryant, Cody↗

NASA NTRSDate not supplied

Fostering Open Science in Earth Data Science Research: Insights From Earthdata Forum By ASDC

In the dynamic landscape of Earth Science research, the promotion of open science principles is paramount for advancing knowledge and collaboration. The Earthdata Forum is an actively maintained and operational user forum for all participating National Aeronautics and Space Administration (NASA) Earth Observing System Data and Information System (EOSDIS) Distributed Active Archive Centers (DAACs), and the Global Change Master Directory (GCMD). The Forum serves as a cross-DAAC platform from which user communities can obtain authoritative information relating to NASA Earth Science. This abstract explores the role of the Earthdata Forum forum.earthdata.nasa.gov as a pivotal platform in fostering open science within the Earth Science community. The platform serves as a hub for researchers to actively engage in discussions, share datasets, and collaboratively tackle challenges in the field. Key aspects discussed include the platform's contribution to data accessibility, collaboration, and knowledge sharing. Forum.earthdata.nasa.gov provides a space where researchers transparently ask questions, discuss methodologies, share insights, and seek advice from a vibrant community. The resulting collaborative environment not only facilitates the exchange of ideas but also bolsters the collective knowledge base.

Earthdata FORUM↗

DOE OSTI2021

Machine Intelligence to Detect, Characterise, and Defend against Influence Operations in the Information Environment

Social media has enabled a new era of manipulation in the information and cognitive domains. Deceptive content—misleading, falsified, and fabricated—is routinely created and spread in the modern social media environment with the intent to create confusion and widen political and social divides, and exploit the societal conflict exacerbated by these divides in the real-world (aka physical domain). Such disinformation campaigns demonstrate a threat to the integrity of economic, political, cultural, public health, and national security institutions around the world. In this work we overview our artificial intelligence (AI) capabilities to detect, describe, and defend against information operations on Twitter as an example social platform to understand the influence of misleading and falsified content diffusion and better enable those charged with defending against such manipulation to enable responsive parties to counter it. We first present novel linguistically-informed deep learning (DL) models for misinformation and disinformation detection, and present an in-depth linguistic analysis of psycho-linguistic markers across broad deception categories. We then demonstrate how our models perform in the multilingual and multimodal setting and categorize falsified and misleading content based on the intent to deceive. We also provide a large-scale analysis to describe user behavior and spread patterns while engaging with deceptive content and report novel findings about the immediate diffusion of deceptive content by characterizing the vulnerable sub-populations and their demographics, and explicitly measuring speed and scale of deception spread to uncover who shares deceptive content, how quickly, how much, and how evenly. In addition, we measure audience reactions to misinformation and disinformation at scale, distinguishing the reactions of users identified as bots versus humans. Finally, we take advantage of deep translation and generation models to create unique solutions for real-time defense against digital deception and discuss how to apply causal inference to prescribe and intervene into strategic communications jointly across information, cognitive, and physical domains.

artificial intelligence, deep learning, neural lan↗

NASA NTRSDate not supplied

NASA Institutional Scientific Collection and Biospecimen Sharing Program at Ames Research Center

For decades, NASA and their international partners have flown and conducted non-human biological experiments in space to understand the effects of spaceflight and address potential biological hazards. It is imperative to understand the basic science and health risks associated with spaceflight, along with developing countermeasures, as humanity ventures back to the Moon, and then to Mars and beyond. Sending organisms into space is a costly endeavor which makes space-flown biological specimens a valuable resource. To enable maximum scientific return, samples not required by the Principal Investigators are harvested, preserved and archived in NASA’s Institutional Scientific Collection (ISC) at Ames Research Center (ARC). These specimens are collected according to well-established SOPs that maintain their quality and integrity. To enable new discoveries, the samples are then made available to the international scientific community through NASA’s Biospecimen Sharing Program (BSP). The NASA ISC currently stores over 32,000 specimens from Shuttle, International Space Station and ground-based investigations. Tissues are predominantly from mice and rats, though samples are also available from bacteria and quail. The specimens include tissues from all biological systems including musculoskeletal, neurosensory, reproductive, respiratory, circulatory, and digestive and are stored at -80°C, -20°C, +4°C, or ambient. Descriptive metadata is available for all samples. Historically, these tissues have been used for a wide range of analyses, including histology, genomics, and transcriptomics. Also available through the ISC are tissue from NASA’s Space Radiation Laboratory. To study the effects of space radiation, researchers irradiate biological specimens and unused samples are made available through the LSDA. These biospecimens and data are made available through the public Life Sciences Data Archive (LSDA) website to promote basic discovery, pre-clinical and clinical science. Visit the NASA ISC-BSP website for more information. Websites: https://lsda.jsc.nasa.gov/ ; https://www.nasa.gov/ames/research/space-biosciences/isc-bsp

Space Flown Biospecimens↗

DOE OSTI2024

Data-Informed Synthetic Networks of Water Distribution Systems for Resilience Analysis in Puerto Rico

The increasing potential of infrastructure disruptions calls for high-quality infrastructure models to be used in resilience analysis and decision making. Unfortunately, many utilities and communities do not have access to accurate and detailed models due to a lack of data and resources. Furthermore, security restrictions on sharing infrastructure models present roadblocks to research, analysis, and decision making. Recent advances in the development of synthetic water distribution models provide a potential solution to this problem. There is an opportunity to improve these methods by leveraging incomplete pipe datasets to aid synthetic network generation. To address this gap, we developed a methodology for synthetic network generation that incorporates partial pipe data using a modification of the minimum cost flow algorithm for network generation and pipe sizing. This methodology demonstrates how partial pipe data can be leveraged to improve site-specific synthetic network generation. For the study area of Mayagüez, Puerto Rico, a synthetic model generated using 50% of real pipe data matches the pressure of the validation system with an average error of 23.5 m of head, which improves upon the average error of 31.6 m of head produced by a synthetic model generated using no data of the real pipes. Additionally, synthetic networks are shown to replicate the pressure response under a disruption scenario of the validation network, suggesting potential use in resilience analysis.

resilience analysis↗

DOE OSTI2025

Machine learning guided selection of broad-spectrum epitope-specific functional antibodies for "Disease X"

Our project established and demonstrated a transfer learning framework that enables prediction of antibody–antigen interactions across related viruses. The approach focused on three major activities: 1. Conserved region and epitope identification – We compared viral protein structures and sequences to identify shared receptor-binding domains and neutralizing epitope regions across variants and related viruses. These conserved features formed the foundation for discovering broadly functional antibodies. 2. Machine learning model development – We built neural network–based models that integrate epitope features with antibody sequence information. Instead of relying solely on structural or physical properties, the models learned transferable patterns that describe antibody binding potential across different viral families. 3. Transfer learning and validation – Using SARS-CoV-2 and Ebola as source systems, we successfully transferred learned epitope features to predict antibody interactions for SARS CoV-1 and Marburg virus. Iterative cycles of dataset generation, retraining, and evaluation improved generalization and predictive power, ensuring the framework can adapt to new threats.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

CLM5 Simulations of Soil Moisture and Grain Carbon for CONUS at 0.125 degrees

This dataset provides 0.125° gridded simulations of soil moisture and crop grain carbon for the Contiguous United States (CONUS), generated using the Community Land Model version 5 (CLM5) with the biogeochemistry module enabled. The data covers a historical baseline (1980–2015) and mid-century future projections (2020–2055). Future projections are organized into two sets of scenarios to distinguish the impacts of different drivers: (1) Atmospheric Only (ATM-only): These scenarios apply future atmospheric forcings while holding land use and land cover change (LULCC) at historical baseline levels. The atmospheric forcings represent moderately versus severely hotter/drier atmospheric conditions (dynamically downscaled perturbed thermodynamics simulations based on CMIP6 SSP245 and SSP585 warming signals), each with cooler versus hotter Earth System Model temperature sensitivity instantiations. These scenarios are identified in the file names as atm45cooler, atm45hotter, atm85cooler, and atm85hotter. (2) Coupled Atmospheric and Land-Use (LAND+ATM): These scenarios apply future atmospheric forcing together with future LULCC by pairing atmospheric pathways with lower versus higher population/economic growth scenarios representing Shared Socioeconomic Pathways 3 and 5 (SSP3 and SSP5). These scenarios are identified in the file names as atm45cooler_ssp3, atm45hotter_ssp3, atm45cooler_ssp5, atm45hotter_ssp5, atm85cooler_ssp3, atm85hotter_ssp3, atm85cooler_ssp5, and atm85hotter_ssp5. Please refer to the README file for detailed information on file structure, variables, units, and data formats.

Yao, Lili [Pacific Northwest National Laboratory] ↗

DOE OSTI2026

CLM5 Simulations of Soil Moisture and Gross Primary Productivity for CONUS at 0.125 degrees

This dataset provides 0.125-degree gridded simulations of soil moisture and gross primary productivity (GPP) for the Contiguous United States (CONUS), generated using the Community Land Model version 5 (CLM5) with the biogeochemistry module enabled. The data covers a historical baseline (1980-2015) and mid-century future projections (2020-2055). Future projections are organized into two sets of scenarios to distinguish the impacts of different drivers: (1) Atmospheric Only (ATM): These scenarios apply future atmospheric forcings while holding land use and land cover (LULC) at historical baseline levels. The atmospheric forcings represent moderately versus severely hotter/drier atmospheric conditions (dynamically downscaled perturbed thermodynamics simulations based on CMIP6 SSP245 and SSP585 warming signals), each with cooler versus hotter Earth System Model temperature sensitivity instantiations. These scenarios are identified in the folder names as rcp45_cooler_near, rcp45_hotter_near, rcp85_cooler_near, and rcp85_hotter_near. (2) Coupled Atmospheric and Land-Use (LAND+ATM): These scenarios apply future atmospheric forcing together with future LULC by pairing atmospheric pathways with lower versus higher population/economic growth scenarios representing Shared Socioeconomic Pathways 3 and 5 (SSP3 and SSP5). These scenarios are identified in the file names as ssp3_rcp45_cooler_near, ssp3_rcp45_hotter_near, ssp5_rcp85_cooler_near, and ssp5_rcp85_hotter_near. Please refer to the "README_first.md" file for detailed information on file structure, variables, units, and data formats.

drought↗