Science Support Framework elements: Data management, publishing and sharing, preservation

Product types: Publication, data release

Impact: Increases accessibility and interoperability of magnetotelluric (MT) data (spatiotemporal and geophysical data; fig. 2)

What need does the project address? MT is an electromagnetic method used in geophysics that is sensitive to variations in subsurface electrical resistivity. Metadata standards, archivable and exchangeable file formats, and open-source software tools supports reading and writing of MT data files. These capabilities also support the USGS and IRIS because the USGS has an obligation to U.S. Congress to collect MT data across the southwestern United States and archive those data to IRIS.

How do project outcomes address the need? Outcomes of this project include a metadata standard for MT time-series data, an open-source Python package to read and write standard metadata (mt-metadata), and an open-source Python package (MTH5) that reads and writes a standard HDF5 file with standard metadata. These outcomes allow MT data to be archived in a standard way, regardless of the data repository, while propelling MT time-series data to follow FAIR principles.

How can users take advantage of project outcomes? Users may read the publication “MTH5—An Archive and Exchangeable Data Format for Magnetotelluric Time Series Data” (Peacock and others, 2022), view the metadata standards (Peacock and others, 2021), and view the open-source software tools mt-metadata (https://code.usgs.gov/gmeg/mt-metadata) and MTH5 (https://code.usgs.gov/gmeg/mth5). An example workflow is demonstrated in figure 2.

In figure 2, magnetotelluric (MT) data collected by different users and instruments are formatted into a Hierarchical Data Format 5 (HDF5) standard using the open-source Python package, MTH5. The associated package, mt-metadata, provides tools to read and write standardized MT metadata. Data can be archived at the Incorporated Research Institutions for Seismology (IRIS) Data Management Center (DMC), U.S. Geological Survey (USGS) ScienceBase, or other data repositories. These data can be input into data-processing software.

Science Support Framework elements: Processing, web services, publishing and sharing

Product types: Software release, data release, web service

Impact: Creates actionable data that fill a gap in USGS basin-characteristic products (fig. 3).

What need does the project address? The project addresses consistent and seamless basin characteristics, describing the defining features of watersheds that are not available nationally at a 30-meter resolution. These data are useful for machine learning, statistical modeling, and process-based hydrologic modeling. Traditional methods for generating basin characteristics are time and computationally intensive, which limits their use for widespread applications without significant investment.

How do project outcomes address the need? We formalized a method to pre-compute basin characteristics for the contiguous United States using previously available flow-direction grids. We then used this method to produce a pilot dataset of nationally consistent, pre-computed basin characteristics at a 30-meter resolution, called flow-conditioned parameter grids (fig. 3).

How can users take advantage of project outcomes? The software to precompute basin characteristics is available as a USGS software release (Barnhart and others, 2020) and the pilot dataset of pre-computed basin characteristics may be accessed through a USGS data release (Barnhart and others, 2021). Additionally, queries against the pilot, pre-computed basin-characteristics grid may be made through a USGS StreamStats API service, found at this link: https://streamstats.usgs.gov/docs/streamstatsservices/#/.

Science Support Framework elements: Data, processing, communities of practice

Product types: Data release, publication

Impact: Enables fish-population tracking for fish management and conservation through artificial-intelligence analysis of fish imagery (fig. 4).

What need does the project address? Artificial intelligence (AI) is revolutionizing ecology and conservation by enabling the identification and recognition of individual animals in nature from photos and videos (fig. 4). Our project develops training data and code to advance this technology for freshwater-fish management and conservation. Specifically, individual-fish recognition with AI would enable mark-recapture models to assess population size, growth, and movement dynamics.

How do project outcomes address the need? We developed training data and source code for individual fish recognition using a deep-learning framework (convolutional neural networks). Through the application of transfer-learning methods, we achieved predictive accuracy of nearly 90 percent for individual Brook trout classification from images.

How can users take advantage of project outcomes? Training data are available in the data releases “Annotated Fish Imagery Data for Individual and Species Recognition with Deep Learning” (Hitt and others, 2021a) and “Brook Trout Imagery Data for Individual Recognition with Deep Learning” (Hitt and others, 2022). The work is described in the publications “Pigmentation-Based Visual Learning for Salvelinus Fontinalis Individual Recognition” (Zhou and others, 2022) and “Comparison of Underwater Video with Electrofishing and Dive-Counts for Stream Fish Abundance Estimation” (Hitt and others, 2021b).

Science Support Framework elements: Analysis, applications, processing

Product types: Data release, publication

Impact: Informs the projection of water use and irrigation demand in the Delaware River Basin (fig. 5).

What need does the project address? The intersection of water-resource use and availability with anthropogenic land use and land management strongly affects economic vitality, environmental health, and human health and well-being. Integrated modeling can address the feedbacks between water, land, and climate processes, producing actionable information that can be used by land managers and decision-makers to anticipate and plan for future scenarios of natural and anthropogenic Earth-system change (Dornbierer and others, 2021b).

How do project outcomes address the need? The linkage of three previously disparate efforts—remote-sensing-based research on evapotranspiration and water use, land-use modeling (fig. 5), and runoff and recharge modeling—connects the vital elements of the water budget and increases our understanding of the land-water-climate system. This project serves as a platform for the continued development of an interdisciplinary predictive-modeling capability within USGS and breaks down the conceptual, practical, and technical challenges in modeling the integrated Earth system.

How can users take advantage of project outcomes? A complete suite of historical, current, and future land-use and land-cover data for the Delaware River Basin are available to download, with maps at 10-year intervals from 1680–2100 and multiple scenarios available from 2018–2100; similarly, crop water-use estimates and irrigation requirements have been produced for the concurrent land-cover distributions from 1950–2100. These data are distributed alongside the companion land-use and land-cover data (Dornbierer and others, 2021a) and described in Dornbierer and others (2021b).

Science Support Framework elements: Analysis, information, publishing and sharing

Product types: Data release, publication

Impact: Improves hydrologic modeling with a national map layer of tile drainage (fig. 6).

What need does the project address? Tile drainage is a type of subsurface drainage system that removes excess soil water from agricultural fields. Every state in the U.S. has agricultural fields with tile drainage and they are most common in the poorly drained fields of the Midwest (U.S. Department of Agriculture, 2024). Without knowing how tile-drain extent has changed over time, it is difficult to differentiate how streamflow and water quality have changed because of spatial extent and characteristics of tile-drain networks. Our method delineates tile drains in satellite imagery, providing a way to look at historical imagery and use satellite data to maintain an up-to-date geospatial layer of tile-drain extents in basins of interest. In figure 6, on the right panel, the white lines are the delineated tile-drain locations, while the black areas denote the spacing between individual tile-drain lines. The spacing is one of the ways in which tile drainage has changed in recent decades.

How do project outcomes address the need? A fully convolutional neural network (U-Net) was trained to delineate tile drains in satellite imagery by incorporating methods from Ronneberger and others (2015) and Zhang and others (2018). A Jupyter Notebook (which can be run locally or in the cloud) is used to pre-process panchromatic satellite images (approximately 0.5 meters ground resolution) to create the format for processing (0.5 km² patches). The model is then run for the group of pre-processed data and produces a binary raster of “tile” and “not tile” for each patch (fig. 6). These patches are then reintegrated to the original spatial extent of the satellite imagery.

How can users take advantage of project outcomes? Training data are available in an image library (Williamson and Hoefling, 2023), which includes tile-drained landscapes, traced tile-drained landscapes, and examples of tile-drain types. This image library documents the initial data used to train the model and provides an example of how additional images could be integrated to improve the model. The work is described in “Machine-Learning Model to Delineate Sub-Surface Agricultural Drainage from Satellite Imagery” (Redoloza and others, 2023).

Science Support Framework elements: Publishing and sharing, planning, applications

Product types: Publication

Impact: Increases efficiency and streamlines production of decision-support tools at the USGS (fig. 7).

What need does the project address? There is a need across the USGS to streamline and implement best practices in decision-support system development. Decision-support system development may require substantial investments of time and money throughout a project’s lifecycle, as well as a host of decisions in design, science output, and user and stakeholder engagement. Across the USGS, significant expertise and experience exists in developing decision support systems, but there is a need to facilitate the sharing of the knowledge learned.

How do project outcomes address the need? By use of an online survey (54 responses) and semi-structured interviews (23 interviews), the project team collected empirical qualitative, and quantitative data about USGS scientists’ experiences designing and implementing decision-support systems. This dataset provides an opportunity to identify what is working well and what could be improved upon with the USGS decision-support tool efficiency.

How can users take advantage of project outcomes? Stoltz and others (2023) summarizes the recommendations, best practices, and lessons learned from decision support system developers across the USGS (fig. 7).

Science Support Framework elements: Publishing and sharing, data, semantics

Product types: Publication

Impact: Makes eDNA occurrence data more accessible and interoperable (fig. 8).

What need does the project address? Environmental DNA (eDNA) studies have allowed for the identification and biosurveillance of numerous invasive and threatened aquatic species. This information supports managers in their decision-making efforts which requires that data and metadata be produced and reported in an accurate and standardized fashion to improve confidence in the results.

How do project outcomes address the need? The project team developed robust standards for accurate data, while allowing flexibility in the protocols. The project team worked to gain the Department of the Interior and community consensus on eDNA standards for public display via seven town halls, four invited reviews, and developed a process for submitting eDNA data to the USGS Nonindigenous Aquatic Species (NAS) database (U.S. Geological Survey, 2023a) for visualization through an online map viewer (fig. 8). NAS currently houses and displays visual accounts of nonindigenous species and allows for predictions of species spread and movement corridors. The expansion of the database ensures that eDNA data is FAIR, providing a findable source of data that is accessible to the public and managers alike, interoperable for use with existing and future management tools, and ensures reusability for downstream-secondary analyses, such as the development of species distribution models.

How can users take advantage of project outcomes? These data can be used to inform decision-makers regarding the initiation of rapid-response efforts to invasive species, to improve the estimation of cryptic species occurrence rates, to map invasion pathways, and to improve monitoring of eradication efforts. Researchers can also use the data to inform the design of future studies. An explanation about eDNA in the NAS database is available at U.S. Geological Survey (2023a). A description of the process of developing the standard for NAS is documented in “Gaining Decision-Maker Confidence Through Community Consensus—Developing Environmental DNA Standards for Data Display on the USGS Nonindigenous Aquatic Species Database” (Ferrante and others, 2022).

Science Support Framework elements: Applications, processing, science-project support

Product types: Data release, code repository

Impact: Provides actionable data for wildland fire-recovery efforts (fig. 9).

What need does the project address? Wildfires affect streams and rivers when they burn vegetation and scorch the ground, making floods more likely to happen and reducing water quality (Murphy and others, 2023). Timely information before and after a fire can assist public managers, first responders, fire scientists, and hydrologists with mitigating floods and planning proper water-treatment strategies.

How do project outcomes address the need? The National StreamStats Beta Application (StreamStats Team, 2022) provides a platform for users to delineate basins, compute percent-burned area in a basin, and trace an imaginary raindrop’s path from the edge of a fire to the nearest water-course and then downstream (fig. 9). This project added the equations from Moody (2012). Users can now calculate additional basin characteristics and estimate post-wildfire peak flows in streams and rivers for the Upper Colorado and Gunnison River Basins.

How can users take advantage of project outcomes? The information is available through the National StreamStats Beta Application at StreamStats Team (2022). Underlying geographic information systems (GIS) layers are available through the data release “Basin Characteristic Layers for the Upper Colorado & Gunnison Rivers Pilot Project for StreamStats 2020” (Kolb and others, 2021), and project code is available at https://code.usgs.gov/StreamStats/clients/StreamStats-National. More information about the National Streamflow Statistics Program (NSS) is found at McCarthy (2019).

Science Support Framework elements: Applications, analysis, data

Product types: Web link, code repository

Impact: Delivers real-time coastal drought conditions to climatologists and coastal-resource managers (fig. 10).

What need does the project address? Many coastal ecosystems are experiencing departures from historical salinity conditions due to changing land use (such as channelization and urbanization) and climate patterns (including increased frequency, severity, or duration of floods and droughts). Coastal habitats, biota, and water resources are negatively impacted by increased frequency and severity of extreme salinity-disturbance events.

How do project outcomes address the need? The U.S. Geological Survey developed the Coastal Salinity Index (CSI) to identify and communicate salinity anomalies (disturbance events) through quantitative analyses of long-term salinity records. This project makes the CSI useful as a monitoring, forecasting, and decision-making tool, extending the existing web platform to enable real-time reporting of disturbance events as they unfold throughout the Eastern Atlantic and Gulf of Mexico coastlines of the United States (fig. 10). The project added the new ability to analyze spatial dynamics in salinity anomalies, which can be compared with concurrent terrestrial drought monitoring maps. Data from the USGS, the National Park Service (NPS), and the National Estuarine Research Reserve System (NERRS) gages are integrated into the website.

How can users take advantage of project outcomes? An online data portal allows real-time dissemination and graphical presentation of CSI calculations, depicting the effects of changing salinity that impact coastal estuarine ecosystems, fish habitat, and freshwater availability for municipal and industrial use. The CSI R package and associated scripts are available at McCloskey (2023).

Science Support Framework elements: Applications, planning, communities of practice

Product types: Web link, publication

Impact: Provides predictions about grassland productivity to decision makers (fig. 11).

What need does the project address? Rangeland ecosystems are one of the largest single providers of agroecological services in the United States. The plant growth of these rangelands helps determine the amount of forage available for livestock and wildlife and determines the potential for fire likelihood and plant restoration success. Every spring, rangeland managers face the same difficult challenge—trying to approximate how much and where grass will be available during the upcoming growing season. Accordingly, a predictive understanding of the upcoming growing season’s rangeland production could greatly support their decisions about where and at what stocking rates to allow livestock on public and private lands and for prioritizing limited resources for wildlife health, restoration efforts, and (or) fire planning.

How do project outcomes address the need? We have created a rangeland productivity forecasting tool for the southwestern United States. This tool gives intuitive visual representations of forecasts for the upcoming growing seasons’ productivity (fig. 11). Data in the tool are updated every two weeks so the forecasts can gain accuracy as the growing season progresses. This tool allows public land managers, ranchers, and the public to easily access expected grassland growth to monitor conditions in other regions in the southwestern United States. The tool is being used by the Bureau of Land Management (BLM), the Bureau of Indian Affairs (BIA), the U.S. Fish and Wildlife Service (FWS), the National Resource Conservation Service (NRCS), numerous state agencies, and insurance companies.

How can users take advantage of project outcomes? The tool is free and publicly available at https://grasscast.unl.edu. The science behind the calculation of gross primary productivity is described in the paper “Satellite Solar-Induced Chlorophyll Fluorescence and Near-Infrared Reflectance Capture Complementary Aspects of Dryland Vegetation Productivity Dynamics” (Wang and others, 2022).

Science Support Framework elements: Knowledge management, science-project support, communities of practice

Product types: Software release, publication

Impact: Increases understanding and use of USGS data and of Pangeo (fig. 12).

What need does the project address? USGS scientists increasingly seek to share and collaborate with one another while working on data and code. Jupyter Notebooks are a tool that allow people to share data and code, and Pangeo is a platform that allows people to use Jupyter Notebooks in the USGS cloud; however, few examples exist on how to use Pangeo and no formal documentation exists for USGS scientists on how to use Pangeo.

How do project outcomes address the need? We created curated examples of the use of Jupyter Notebooks, showcasing interdisciplinary examples across the USGS (fig. 12). We also created tutorials on how to use USGS Pangeo and Jupyter Notebooks.

How can users take advantage of project outcomes? Users can utilize the Jupyter Notebook examples and tutorials to create unique data stories and document reusable workflows for their own work (Erickson and others, 2020). Erickson and others (2021) summarizes lessons from the project.

Science Support Framework elements: Knowledge management, information, applications

Impact: Helps compile information for USGS employees to utilize the cloud environment (fig. 13)

What need does the project address? The USGS Cloud Environment Cookbook addressed the need for USGS employees to have a web space to share their experience working with a new technical environment. The goal was to provide a centralized location where it was easy for users to contribute cloud examples and use cases (fig. 13).

How do project outcomes address the need? The Cookbook provided a forum to share knowledge about working in the USGS cloud environment by using a straightforward “recipe” template and a common lightweight markup language “Markdown”. The recipe for examples included guidance, such as starting with the simplest possible example, then introducing complexities one at a time, linking to other documents rather than re-explaining concepts, describing the problem and not assuming familiarity, and explaining the process rather than just the end result. It also includes guidance explaining the advantages and disadvantages of the strategy, including when it is most appropriate; mentioning alternative solutions; and providing codebases or links necessary for reproducing the example.

How can users take advantage of project outcomes? Users could access the Cookbook to view recipes that had been submitted and find instructions for contributing their own recipes in the project’s code repository. The project has been superseded by USGS Cloud Hosting Solutions site content and is no longer accessible.