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Collection of Publications provided or contributed by SSAR programs. Selecting an item you'll find additional information and program point of contacts.

Filter Total Items: 245

Developing fluvial fish species distribution models across the conterminous United States—A framework for management and conservation

This report explains the steps and specific methods used to predict fluvial fish occurrences in their native ranges for the conterminous United States. In this study, boosted regression tree models predict distributions of 271 ecologically important fluvial fish species using relations between fish presence/absence and 22 natural and anthropogenic landscape variables. Models developed for the fres
Hao Yu, Arthur R. Cooper, Jared Ross, Alexa McKerrow, Daniel J. Wieferich, Dana M. Infante

Assessing the value and usage of data management planning and data management plans within the U.S. Geological Survey

As of 2016, the U.S. Geological Survey (USGS) Fundamental Science Practices require data management plans (DMPs) for all USGS and USGS-funded research. The USGS Science Data Management Branch of the Science Analytics and Synthesis Program has been working to help the USGS (Bureau) meet this requirement. However, USGS researchers still encounter common data management-related challenges that may be
Madison Langseth, Elizabeth Sellers, Grace C. Donovan, Amanda N. Liford

Evaluating deep learning architecture and data assimilation for improving water temperature forecasts at unmonitored locations

Deep learning (DL) models are increasingly used to forecast water quality variables for use in decision making. Ingesting recent observations of the forecasted variable has been shown to greatly increase model performance at monitored locations; however, observations are not collected at all locations, and methods are not yet well developed for DL models for optimally ingesting recent observations
Jacob Aaron Zwart, Jeremy Alejandro Diaz, Scott Douglas Hamshaw, Samantha K. Oliver, Jesse Cleveland Ross, Margaux Jeanne Sleckman, Alison P. Appling, Hayley R. Corson-Dosch, Xiaowei Jia, Jordan S Read, Jeffrey M Sadler, Theodore Paul Thompson, David Watkins, Elaheh (Ellie) White

John Wesley Powell Center for Analysis and Synthesis Newsletter, volume 7, issue 1

The John Wesley Powell Center for Synthesis & Analysis is a USGS initiative that aims to foster innovative thinking in Earth system science through collaborative analysis and synthesis of existing data and information. The Powell Center supports working groups that address some of the most pressing and complex questions facing society, such as climate change, biodiversity loss, water scarcity, nat
Jill Baron, Demi Jasmine Bingham

Help build the Protected Areas Database of the United States (PAD-US)

IntroductionPAD-US provides a comprehensive geospatial database of protected and managed areas in the United States. We assemble known protected areas whose primary purpose is biodiversity conservation, as well as lands and waters that provide public access to nature. As a National Geospatial Data Asset (https://ngda-portfolio-community-geoplatform., the PAD-US database (https://w
Roger M. Johnson

Community for data integration 2019 project report

The U.S. Geological Survey Community for Data Integration annually supports small projects focusing on data integration for interdisciplinary research, innovative data management, and demonstration of new technologies. This report provides a summary of the 14 projects supported in fiscal year 2019 and outlines their goals, activities, and accomplishments. Proposals in 2019 were encouraged to addre
Amanda N. Liford, Caitlin M. Andrews, Aparna Bamzai, Joseph A. Bard, David S. Blehert, John B. Bradford, Wesley M. Daniel, Sara L. Caldwell Eldridge, Frank Engel, Jason A. Ferrante, Amy K. Gilmer, Margaret E. Hunter, Jeanne M. Jones, Benjamin Letcher, Frances L. Lightsom, Richard R. McDonald, Leah E. Morgan, Sasha C. Reed, Leslie Hsu

Preliminary machine learning models of manganese and 1,4-dioxane in groundwater on Long Island, New York

Manganese and 1,4-dioxane in groundwater underlying Long Island, New York, were modeled with machine learning methods to demonstrate the use of these methods for mapping contaminants in groundwater in the Long Island aquifer system. XGBoost, a gradient boosted, ensemble tree method, was applied to data from 910 wells for manganese and 553 wells for 1,4-dioxane. Explanatory variables included soil
Leslie A. DeSimone


No abstract available.
Xiaogang Ma, Matty Mookerjee, Leslie Hsu, Denise Hills

Update on U.S. Geological Survey Fundamental Science Practices

The U.S. Geological Survey (USGS) Fundamental Science Practices (FSP) are a set of standard principles fundamental to how USGS conducts and carries out its science activities and how resulting information products and data are reviewed, approved, and released. These policies, practices, philosophical premises, and operational principles serve as the foundation for all USGS research and monitoring

When less is more: How increasing the complexity of machine learning strategies for geothermal energy assessments may not lead toward better estimates

Previous moderate- and high-temperature geothermal resource assessments of the western United States utilized data-driven methods and expert decisions to estimate resource favorability. Although expert decisions can add confidence to the modeling process by ensuring reasonable models are employed, expert decisions also introduce human and, thereby, model bias. This bias can present a source of err
Stanley Paul Mordensky, John Lipor, Jacob DeAngelo, Erick R. Burns, Cary Ruth Lindsey

Simulating debris flow and levee formation in the 2D shallow flow model D-Claw: Channelized and unconfined flow

Debris flow runout poses a hazard to life and infrastructure. The expansion of human population into mountainous areas and onto alluvial fans increases the need to predict and mitigate debris flow runout hazards. Debris flows on unconfined alluvial fans can exhibit spontaneous self-channelization through levee formation that reduces lateral spreading and extends runout distances compared to unchan
Ryan P. Jones, Francis K. Rengers, Katherine R. Barnhart, David L. George, Dennis M. Staley, Jason W. Kean

Invaders at the doorstep: Using species distribution modeling to enhance invasive plant watch lists

Watch lists of invasive species that threaten a particular land management unit are useful tools because they can draw attention to invasive species at the very early stages of invasion when early detection and rapid response efforts are often most successful. However, watch lists typically rely on the subjective selection of invasive species by experts or on the use of spotty occurrence records.
Catherine S. Jarnevich, Peder Engelstad, Jillian LaRoe, Brandon Hays, Terri Hogan, Jeremy Jirak, Ian Pearse, Janet S. Prevéy, Jennifer Sieraki, Annie Simpson, Jess Wenick, Nicholas Young, Helen Sofaer