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NASA NTRSDate not supplied

"Giovanni at 20: Consistency and Persistency in Making Earth Remote Sensing Data Available (and Useful) to the Earth Science Community"

Since its creation in the year 2000, the NASA Geospatial Interactive Online Visualization ANd aNalysis Infrastructure (Giovanni) has been an exemplar of how to provide remotely-sensed satellite data and related Earth science datasets to a broad and globally diverse researcher community. One of the hallmarks of Giovanni system usage is that it is common to see practitioners of many branches of science – particularly those in fields not traditionally associated with remote-sensing data, such as animal behavior and paleooceanography – both accessing and employing datasets which the system provides. This usage pattern is attested to by the diversity of published science citing Giovanni. Giovanni has evolved through several versions, each of which increased analytical and visualization options while also enhancing ease-of-use. Information resources supporting Giovanni from the Goddard Earth Sciences Data and Information Services Center (GES DISC), which hosts the system, start with basic mapping and plotting functions and extend to “How-to” recipes demonstrating interusability with other data analysis systems and software. Giovanni is now being developed for use in a cloud environment, potentially expanding datasets the system can be applied to, and also improving performance for data having high spatial and temporal resolution. This presentation covers the system’s historical success with interesting examples of Giovanni-citing research, and will apply its current capabilities to a multi-dataset examination of derecho events associated with mesoscale convective phenomena in the summer of 2020, indicating how Giovanni is prepared for ongoing support of Earth science in a changing world.

Giovanni Derecho visualization↗

NASA NTRS2019

Implementing Polar Projections with OGC Services for the Enhancement of AIRS NRT Visualization in LANCE

The Atmospheric Infrared Sounder (AIRS) NRT product is one important element in the Land, Atmosphere Near real-time Capability for EOS (LANCE). The LANCE processing of AIRS NRT products and the image generation are performed at the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). The Open Geospatial Consortium (OGC) services are being utilized to access AIRS NRT images. The ongoing AIRS NRT imagery enhancement work includes adding a new set of the images in polar projections. Polar projections are commonly used for mapping Antarctica and Arctic regions. We have implemented more precise south polar (EPSG:3031) projection and north polar (EPSG:3413) projection making our OGC service instances more useful and interoperable. Thus, AIRS NRT data can be easily accessed and integrated with other applications. It greatly increases the impact of our data on researches in polar regions.In this presentation, we will introduce the optimized processing workflow for OGC services from data access with spatial-temporal index to data visualization with different SLD, and demonstrate how to use open source software to provide more precise map images in polar projections.

Zhao, Peisheng↗

NASA NTRS2012

Tiled WMS/KML Server V2

This software is a higher-performance implementation of tiled WMS, with integral support for KML and time-varying data. This software is compliant with the Open Geospatial WMS standard, and supports KML natively as a WMS return type, including support for the time attribute. Regionated KML wrappers are generated that match the existing tiled WMS dataset. Ping and JPG formats are supported, and the software is implemented as an Apache 2.0 module that supports a threading execution model that is capable of supporting very high request rates. The module intercepts and responds to WMS requests that match certain patterns and returns the existing tiles. If a KML format that matches an existing pyramid and tile dataset is requested, regionated KML is generated and returned to the requesting application. In addition, KML requests that do not match the existing tile datasets generate a KML response that includes the corresponding JPG WMS request, effectively adding KML support to a backing WMS server.

Plesea, Lucian↗

NASA NTRS2012

CometQuest: A Rosetta Adventure

Leon, Nancy J.↗

NASA NTRS2018

The Best Educational Tool for Interdisciplinary Earth Science Giovanni

Accessing and using NASA Earth science data has commonly presented a challenge to many educators and students, due to issues such as heterogeneous data formats, complex data structures, large volumes of data storage, special programming requirements, and diverse analytical software options that often require a significant investment in time and resources, especially for novices. By facilitating data access and evaluation, as well as promoting open access to create a more level playing field for non-funded scientists, NASA Earth observation data can be more readily used for scientific discovery and societal benefits. To advance this goal, the NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC) developed the Geospatial Interactive Online Visualization ANd aNalysis Infrastructure (Giovanni). To date, Giovanni has assisted researchers around the world publish over 1300 peer-reviewed papers in a wide range of Earth science disciplines. In this presentation, we will demonstrate how easy it is to use Giovanni for the rapid creation of many different analyses of both weather and climate events.

interdisciplinary↗

NASA NTRS2019

2018 NISAR Applications Workshop: Wetlands; Workshop Report

Wetland ecosystems are a critical part of our natural environment, providing socioeconomic benefits to human communities and habitats to a rich diversity of plant and animal life. Socioeconomic benefits include improved water quality, flood control, foods, shoreline stabilization, groundwater recharge, and recreational opportunities. Wetlands also have a major role as carbon sinks and sources through processes that are influenced by the duration and timing of soil saturation and inundation. Thus, carbon and water cycle models must take into account wetland extent and seasonal patterns of wetland inundation. The joint NASA, US Geological Survey (USGS) and Fish and Wildlife Service (FWS) workshop focused on advancing wetland applications of the spaceborne NASA-ISRO Synthetic Aperture Radar (SAR) mission (NISAR), a jointly developed satellite between NASA and the Indian Space Research Organisation (ISRO) expected for launch early 2022. Participants from 15 national and international organizations --including US Federal Agencies, nonprofits, academics, and the private sector-- had been identified as key-players in facilitating integration of Earth Observations into decision support workflows. Discussions were held over two and a half days to convey the knowledge and measurement needs of the wetlands community and discuss the delivery of relevant geospatial products that could be derived from NISAR data. While the community typically characterizes wetlands by their hydrological process, vegetation and soil types, a central defining characteristic is that a wetland is a land area inundated or saturated in the rootzone for at least 2 weeks of the average vegetation growing season.

FWS↗

NASA NTRSDate not supplied

Informing Disaster Response through Earth Observation: A User-Centric Model for Enhancing Disaster Response Using Geospatial Assets

Satellite observations can provide critical insights to building situational awareness and filling data gaps during disaster response. The National Aeronautics and Space Administration (NASA) Earth Science Division’s Disasters Program aims to advance Earth science data and information to support management decisions that prevent or mitigate the impacts of disasters. In support of this goal, NASA’s Disasters Response Coordination System (DRCS) employs a “One NASA” approach to coordinate and mobilize the Agency’s assets and expertise to provide geospatial information during disasters. The DRCS advances the utility of Earth observation information for supporting disaster response needs and builds skills in the emergency management and disaster response communities through improved coordination, engagement, and learning. The DRCS aims to support reduction of impact to lives and livelihoods by empowering communities to more effectively respond to disasters. The DRCS is organized across six NASA centers and managed by a project office located at NASA Langley, working in alignment with NASA Headquarters. The DRCS employs a user-centered framework that begins with requests from disaster responders (state, local, federal government and non-profits working at a national scale) and ends with after-action assessments that feed lessons learned and process improvements. This poster introduces the DRCS model and approach to expanding the use of Earth observations and geospatial information to support disaster response, share use cases for recent event activations working with decision-making organizations, and highlight initial lessons learned.

Remote Sensing↗

DOE OSTI2024

Machine-Learning-Based Mapping and Modeling of Solar Energy with Ultra-High Spatiotemporal Granularity

Despite the rapid growth of solar energy, we still lack a dynamic, high-fidelity database that tracks the spatiotemporal variations of solar PVs and their associated infrastructures across different places at a spatially resolved scale. The absence of such data presents a barrier to various applications such as solar PV growth projection, solar energy integration, solar incentive design, and climate risk assessment. In this project, we aim to bridge this gap by developing AI-based algorithms to extract granular information about solar PV installations and their associated infrastructures (i.e., distribution grids) from widely available unstructured data like remote sensing images and street views. As a result, we have built the Solar Energy Atlas, a fine-grained, large-scale geospatial overlay of distributed solar PVs and distribution grids. On top of it, we have advanced the understanding of solar adoption and distribution grid vulnerability to climate-induced extremes. Our major contributions can be summarized as follow: (1) By developing new AI algorithms, we have built the most comprehensive solar PV spatiotemporal database covering the entire US. This is the first time we obtained the exact GPS locations, size, subtype, and installation year information for rooftop solar PVs across the US. This database can be used for solar PV growth projection, solar energy integration, solar energy policy analysis and design, and spatially-resolved climate risk assessment. (2) Leveraging this database, we have uncovered the socioeconomic driving factors that are correlated with earlier onset of solar adoption and higher saturated adoption levels. We have identified the heterogeneity in the effects of different types of financial incentives on solar adoption and provided implications for tailoring incentive design based on local income levels to promote equitable solar adoption. (3) We have developed a distribution grid GIS mapping algorithm which can obtain granular geospatial and topology information about distribution grids using multi-modal open data, reducing the dependency on hard-to-obtain smart meter data of conventional approaches. It shows effectiveness in both the U.S. and Sub-Saharan Africa. Using this algorithm, we have uncovered the non-uniform vulnerability of distribution grids to wildfires in California in the aspects of undergrounding protection and Distributed Energy Resources (DER) preparedness. This has provided important implications for improving the affordability and equity of grid adaptation approaches. (3) We have made our produced database publicly available and provided user-friendly interface to enable various stakeholders and the general public to interact with the data. We have also integrated the produced data into the Data Commons platform to enable the public to access the data and correlate it with other location-specific characteristics simply using natural language as queries. The impact of our project is three-fold: (1) New algorithms for mapping solar PVs and distribution grids across space and time, which are open source to facilitate researchers and industry; (2) New databases of solar PVs and distribution grids that have been made publicly available for engineering, social, and policy applications; (3) New understandings and actionable insights on the potential approaches to promoting solar adoption and reducing energy infrastructure vulnerabilities. In this report, we start by discussing the project background and motivation (section 5), followed by the overview of project objectives (section 6). Results and discussion for each task are presented in section 7. Significant accomplishments are summarized in section 8. This report will be concluded by discussing the paths forwards (section 9), products (section 10), and team roles (section 11).

14 SOLAR ENERGY↗

NASA NTRS2015

Cloud-to-Ground Lightning Estimates Derived from SSMI Microwave Remote Sensing and NLDN

Lightning observations are collected using ground-based and satellite-based sensors. The National Lightning Detection Network (NLDN) in the United States uses multiple ground sensors to triangulate the electromagnetic signals created when lightning strikes the Earth's surface. Satellite-based lightning observations have been made from 1998 to present using the Lightning Imaging Sensor (LIS) on the NASA Tropical Rainfall Measuring Mission (TRMM) satellite, and from 1995 to 2000 using the Optical Transient Detector (OTD) on the Microlab-1 satellite. Both LIS and OTD are staring imagers that detect lightning as momentary changes in an optical scene. Passive microwave remote sensing (85 and 37 GHz brightness temperatures) from the TRMM Microwave Imager (TMI) has also been used to quantify characteristics of thunderstorms related to lightning. Each lightning detection system has fundamental limitations. TRMM satellite coverage is limited to the tropics and subtropics between 38 deg N and 38 deg S, so lightning at the higher latitudes of the northern and southern hemispheres is not observed. The detection efficiency of NLDN sensors exceeds 95%, but the sensors are only located in the USA. Even if data from other ground-based lightning sensors (World Wide Lightning Location Network, the European Cooperation for Lightning Detection, and Canadian Lightning Detection Network) were combined with TRMM and NLDN, there would be enormous spatial gaps in present-day coverage of lightning. In addition, a globally-complete time history of observed lightning activity is currently not available either, with network coverage and detection efficiencies varying through the years. Previous research using the TRMM LIS and Microwave Imager (TMI) showed that there is a statistically significant correlation between lightning flash rates and passive microwave brightness temperatures. The physical basis for this correlation emerges because lightning in a thunderstorm occurs where ice is first present in the cloud and electric charge separation occurs. These ice particles efficiently scatter the microwave radiation at the 85 and 37 GHz frequencies, thus leading to large brightness temperature depressions. Lightning flash rate is related to the total amount of ice passing through the convective updraft regions of thunderstorms. Confirmation of this relationship using TRMM LIS and TMI data, however, remains constrained to TRMM observational limits of the tropics and subtropics. Satellites from the Defense Meteorology Satellite Program (DMSP) have global coverage and are equipped with passive microwave imagers that, like TMI, observe brightness temperatures at 85 and 37 GHz. Unlike the TRMM satellite, however, DMSP satellites do not have a lightning sensor, and the DMSP microwave data has never been used to derive global lightning. In this presentation, a relationship between DMSP Special Sensor Microwave Imager (SSMI) data and ground-based cloud-to-ground (CG) lightning data from NLDN is investigated to derive a spatially complete time history of CG lightning for the USA study area. This relationship is analogous to the established using TRMM LIS and TMI data. NLDN has the most spatially and temporally complete CG lightning data for the USA, and therefore provides the best opportunity to find geospatially coincident observations with SSMI sensors. The strongest thunderstorms generally have minimum 85 GHz Polarized Corrected brightness Temperatures (PCT) less than 150 K. Archived radar data was used to resolve the spatial extent of the individual storms. NLDN data for that storm spatial extent defined by radar data was used to calculate the CG flash rate for the storm. Similar to results using TRMM sensors, a linear model best explained the relationship between storm-specific CG flash rates and minimum 85 GHz PCT. However, the results in this study apply only to CG lightning. To extend the results to weaker storms, the probability of CG lightning (instead of the flash rate) was calculated for storms having 85 GHz PCT greater than 150 K. NLDN data was used to determine if a CG strike occurred for a storm. This probability of CG lightning was plotted as a function of minimum 85 GHz PCT and minimum 37 GHz PCT. These probabilities were used in conjunction with the linear model to estimate the CG flash rate for weaker storms with minimum 85 GHz PCTs greater than 150 K. Results from the investigation of CG lightning and passive microwave radiation signals agree with the previous research investigating total lightning and brightness temperature. Future work will take the established relationships and apply them to the decades of available DMSP data for the USA to derive a map of CG lightning flash rates. Validation of this method and uncertainty analysis will be done by comparing the derived maps of CG lightning flash rates against existing NLDN maps of CG lightning flash rates.

Winesett, Thomas↗

DOE OSTI2024

The Integration and Mapping of an Open-Source National Well Resource to Inform Geologic Carbon Storage Site Selection and Risk Prevention: The CO2-Locate Database

Geologic carbon storage (GCS) offers a way to capture and permanently store CO₂ from fossil fuel operations in underground geologic structures, aiding in the transition to a carbon-neutral energy economy. However, CO₂ injection sites can experience gas leakage through existing wells that penetrate storage reservoirs, making knowledge of well locations and characteristics crucial for permitting, infrastructure reusability, and risk assessment in GCS. Currently, public wellbore data from state, federal, and tribal entities are inconsistent and fragmented, with gaps and redundancies. To address this, the National Energy Technology Laboratory (NETL) developed CO2-Locate, an open-source, geospatial database and online application. CO2-Locate integrates over 50 data sources from federal, state, and tribal entities, creating a standardized national well database. Funded by the Bipartisan Infrastructure Law, the database is publicly available through the Energy Data eXchange (EDX) and viewable via the CO2-Locate web mapping application. This tool allows users to query, filter, and visualize well data to support GCS planning, permitting, and risk assessments. This presentation covers the methods used to create CO2-Locate, including data acquisition, processing, attribute mapping, and integration, much of which is automated for future updates. The web mapping application and its role in GCS site selection will also be discussed.

Tetteh, Daniel A.↗

NASA NTRS2014

Enhancement of Mutual Discovery, Search, and Access of Data for Users of NASA and GEOSS-Cataloged Data Systems

An ongoing NASA-funded Data Rods (time series) project has demonstrated the removal of a longstanding barrier to accessing NASA data (i.e., accessing archived time-step array data as point-time series) for selected variables of the North American and Global Land Data Assimilation Systems (NLDAS and GLDAS, respectively) and other NASA data sets. Data rods are pre-generated or generated on-the-fly (OTF), leveraging the NASA Simple Subset Wizard (SSW), a gateway to NASA data centers. Data rods Web services are accessible through the CUAHSI Hydrologic Information System (HIS) and the Goddard Earth Sciences Data and Information Services Center (GES DISC) but are not easily discoverable by users of other non-NASA data systems. An ongoing GEOSS Water Services project aims to develop a distributed, global registry of water data, map, and modeling services cataloged using the standards and procedures of the Open Geospatial Consortium and the World Meteorological Organization. Preliminary work has shown GEOSS can be leveraged to help provide access to data rods. A new NASA-funded project is extending this early work.

data access↗

NASA NTRS2006

Integrating Ideas for International Data Collaborations Through The Committee on Earth Observation Satellites (CEOS) International Directory Network (IDN)

The capabilities of the International Directory Network's (IDN) version MD9.5, along with a new version of the metadata authoring tool, "docBUILDER", will be presented during the Technology and Services Subgroup session of the Working Group on Information Systems and Services (WGISS). Feedback provided through the international community has proven instrumental in positively influencing the direction of the IDN s development. The international community was instrumental in encouraging support for using the IS0 international character set that is now available through the directory. Supporting metadata descriptions in additional languages encourages extended use of the IDN. Temporal and spatial attributes often prove pivotal in the search for data. Prior to the new software release, the IDN s geospatial and temporal searches suffered from browser incompatibilities and often resulted in unreliable performance for users attempting to initiate a spatial search using a map based on aging Java applet technology. The IDN now offers an integrated Google map and date search that replaces that technology. In addition, one of the most defining characteristics in the search for data relates to the temporal and spatial resolution of the data. The ability to refine the search for data sets meeting defined resolution requirements is now possible. Data set authors are encouraged to indicate the precise resolution values for their data sets and subsequently bin these into one of the pre-selected resolution ranges. New metadata authoring tools have been well received. In response to requests for a standalone metadata authoring tool, a new shareable software package called "docBUILDER solo" will soon be released to the public. This tool permits researchers to document their data during experiments and observational periods in the field. interoperability has been enhanced through the use of the Open Archives Initiative s (OAI) Protocol for Metadata Harvesting (PMH). Harvesting of XML content through OAI-MPH has been successfully tested with several organizations. The protocol appears to be a prime candidate for sharing metadata throughout the international community. Data services for visualizing and analyzing data have become valuable assets in facilitating the use of data. Data providers are offering many of their data-related services through the directory. The IDN plans to develop a service-based architecture to further promote the use of web services. During the IDN Task Team session, ideas for further enhancements will be discussed.

Olsen, Lola M.↗

NASA NTRS2016

Explore Earth Science Datasets for STEM with the NASA GES DISC Online Visualization and Analysis Tool, Giovanni

The NASA Goddard Earth Sciences (GES) Data and Information Services Center(DISC) is one of twelve NASA Science Mission Directorate (SMD) Data Centers that provide Earth science data, information, and services to users around the world including research and application scientists, students, citizen scientists, etc. The GESDISC is the home (archive) of remote sensing datasets for NASA Precipitation and Hydrology, Atmospheric Composition and Dynamics, etc. To facilitate Earth science data access, the GES DISC has been developing user-friendly data services for users at different levels in different countries. Among them, the Geospatial Interactive Online Visualization ANd aNalysis Infrastructure (Giovanni, http:giovanni.gsfc.nasa.gov) allows users to explore satellite-based datasets using sophisticated analyses and visualization without downloading data and software, which is particularly suitable for novices (such as students) to use NASA datasets in STEM (science, technology, engineering and mathematics) activities. In this presentation, we will briefly introduce Giovanni along with examples for STEM activities.

precipitation↗

NASA NTRSDate not supplied

RadLab: A Comprehensive Database and Graphical and Programming Interfaces for Biologically Relevant Space Radiation Data

RadLab, a new component of the NASA Open Science Data Repository (OSDR), comprises a database of radiation measurements relevant to space biology, and visual and programmatic interfaces for interrogation and retrieval of these data. The attributes of data available through RadLab include spacecraft, types of radiation sensing instruments, locations within the spacecraft (e.g. modules of the ISS), associated celestial bodies, trajectories, and spacecraft coordinates. The application programming interface (API) implements a request syntax for retrieval of timestamped data filtered by various combinations of such attributes; the graphical user interface (GUI) extends this functionality with visualizations, such as spacecraft schematics, time series plots, geospatial visualizations, and provides easy means to iteratively refine search parameters, inspect the data on the fly, and download target subsets of these data. The release of RadLab currently available to the public contains datasets provided by US and international collaborators and focuses on data recorded on the ISS. Investigators from multiple countries, including the US, Canada, Germany, Bulgaria, Hungary, Italy, Japan, Russia and the Czech Republic, have committed to provide data from their instruments in and beyond low Earth orbit; RadLab will also soon expand to include past (e.g. Shuttle and Mir) and future (e.g. Artemis) data. RadLab will provide a comprehensive, dynamic compendium of space radiation data, enabling the scientific community to perform analyses of data from multiple detectors and to determine the radiation environment of research missions and experiments, both via programmatic retrieval of these data and through the graphical analysis toolkit. The RadLab Working Group has been formed to foster collaborations among data contributors and users, to identify data sources, to put in place standards for data harmonization, and to guide the development of the platform, with the goal to establish the use of RadLab in space radiation research and to advance our understanding of the space radiation environment in human habitats.

radiation↗

NASA NTRSDate not supplied

RadLab: A Comprehensive Database and Graphical and Programming Interfaces for Biologically Relevant Space Radiation Data

RadLab, a new component of the NASA Open Science Data Repository (OSDR), comprises a database of radiation measurements relevant to space biology, and visual and programmatic interfaces for interrogation and retrieval of these data. The attributes of data available through RadLab include spacecraft, types of radiation sensing instruments, locations within the spacecraft (e.g. modules of the ISS), associated celestial bodies, trajectories, and spacecraft coordinates. The application programming interface (API) implements a request syntax for retrieval of timestamped data filtered by various combinations of such attributes; the graphical user interface (GUI) extends this functionality with visualizations, such as spacecraft schematics, time series plots, geospatial visualizations, and provides easy means to iteratively refine search parameters, inspect the data on the fly, and download target subsets. The release of RadLab currently available to the public contains datasets provided by US and international collaborators and focuses on data recorded on the ISS. Investigators from multiple countries, including the US, Canada, Germany, Bulgaria, Hungary, Italy, Japan, Russia and the Czech Republic, have committed to provide data from their instruments in and beyond low Earth orbit; RadLab will also soon expand to include past (e.g. Shuttle and Mir) and future (e.g. Artemis) data. RadLab will provide a comprehensive, dynamic compendium of space radiation data, enabling the scientific community to perform analyses of data from multiple detectors and to determine the radiation environment of research missions and experiments. The RadLab Working Group has been formed to foster collaborations among data contributors and users, to identify data sources, to put in place standards for data harmonization, and to guide the development of the platform, with the goal to establish the use of RadLab in space radiation research and to advance our understanding of the space radiation environment in human habitats.

database↗

NASA NTRS2007

Solutions Network Formulation Report. The Potential Contribution of the Ocean Surface Topography Mission to the General NOAA Oil Monitoring Environment

Data collected by the OSTM could be used to provide a solution for the GNOME DST. GNOME, developed by NOAA?s Office of Response and Restoration Hazardous Materials Response Division, geospatially models oil spill trajectories using wind, current, river flow, and tidal data. Data collected by the OSTM would supply information about ocean currents and wind speeds. This Candidate Solution is in alignment with the Coastal Management, Water Management, Disaster Management, Public Health, Ecological Forecasting, and Homeland Security National Applications and will benefit society by improving the capabilities of emergency responders who evaluate an oil spill?s probable threat.

Hilbert, Kent↗

DOE OSTI2024

AquaPV: Foundational Analysis and Industry Guidance on Floating PV Results

This dataset contains results of the technical potential analysis of floating photovoltaics (FPV) on federally owned or permitted reservoirs in the United States. Estimates of the area of reservoirs that is technically feasible for FPV development are provided for reservoirs that are owned by the US Army Corps of Engineers or the US Bureau of Reclamation, or are associated with hydropower dams licensed by the Federal Energy Regulatory Commission. Other associated data are included, such as estimates of the potential evaporative losses of FPV development, and whether the waterbody is associated with an energy community or disadvantaged community that could qualify it for investment or production tax credits. Data is provided both as a spreadsheet and in a geospatial format including the waterbody geometries. Please read the included readme for more detailed field definitions.

area↗

DOE OSTI2024

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

Detailed description of the dataset sources used in this study, the experimental workflow, and plotting for the paper figures provided at the associated GitHub Meta Repo: https://github.com/IMMM-SFA/Ferencz_et_al_2024_ERL The future water demand projections from this study are hypothetical future water demands that reflect the population and urban land cover changes represented by the scenarios considered. The intent and emphasis of this work is investigating the interactions between population change, evolution of urban morphology, and water demand. These projections are not meant to be likely future demands for specific water providers or the LA region and should not be interpreted as such. The folders contain input and output data for each step of the "Recreate my Experiment" workflow described in the associated GitHub meta-repository as well as data used for plotting Figures for the paper that this dataset supports. Description of each folder's contents and use: Step_1a: Inputs to the associated python script provided on the GitHub repo. Step_1b: Inputs (downscaled population rasters) used by the associated python script provided on the GitHub repo. Original 1-km squared rasters that were downscaled also provided. Step_1c: Urban growth projection rasters corresponding to SSP3 and SSP5 population scenarios are provided in separate subfolders as well as the water provider boundaries used for analysis. Outputs of data processing also provided. Associated python script provided on GitHub. Step_1d: Description of Inputs used by the QGIS Model Builder GUI that automates geospatial processing and clipping the of the high-resolution 60 cm land cover data for each urban land class footprint within a defined polygon boundary. The Model Builder is provided on the GitHub repo and can be used by QGIS. The outputs of this step are in "Clipped Provider Hi Res Landcover". If the user wants to use The Model Builder for different regions of LA or to test our outputs, they will need to download the hi resolution landcover raster listed in the Readme and in Ref [2] of the GitHub Page. Step_1e: All necessary inputs to generate average monthly demand over the 2017-2021 period and the minimum and maximum demands over the 2014-2021 for each water provider. Associated python scripts are on GitHub. Step 2: Output data about land cover metrics (areas and fractions) for each urban land class for each water provider. Associated python script on GitHub. Uses outputs from Step 1d "Clipped Provider Hi Res Landcover" Step 3: Both the Inputs for and Outputs from the urban projection raster analysis Python script on GitHub. The inputs are urban land class rasters for specific SSP and zoning scenarios (low, medium, high) from Step 1c. The outputs are rasters of urban pixels that were converted to a higher land class and the number of land class units that changed (Values of 1, 2, or 3). For example, a value of 2 could be LC 21 -> 23 or LC 22 -> 24. These maps are label "intensification." The other outputs are "urban growth" rasters showing the conversion of non urban to urban land, which are indicated by pixel values of 1. These are used for the urban growth change maps in Figure 3. Step 4: Output projections of indoor and outdoor annual and monthly demands for each water provider for the average, minimum, and maximum monthly demand scenarios for each of the four urban growth scenarios (SSP3 med, SSP5 low, SSP5 med, and SSP5 high). The outputs also include metrics on each water provider used for the demand sensitivity analysis presented in Figure 8. Outputs from Step 4 are used for Figures 4 - 8 of the paper. Figures: This folder has data used for plotting Figures 1 through 5, and 8. Data for Figures 6 and 7 are sourced directly from folders associated with the Processing and Analysis Steps 1 - 4. The GitHub meta repository provides descriptions of how each figure was made and the associated plotting scripts used.

Los Angeles↗