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At least 91 records · Page 5Linked to original sources
USGS2018

Development and release of phenological data products—A case study in compliance with federal open data policy

In Autumn 2015, USA National Phenology Network (USA-NPN) staff implemented new U.S. Geological Survey (USGS) data-management policies intended to ensure that the results of Federally funded research are made available to the public. The effort aimed both to improve USA-NPN data releases and to provide a model for similar programs within the USGS. This report provides an overview of the steps taken to ensure compliance, following the USGS Science Data Lifecycle, and provides lessons learned about the data-release process for USGS program leaders and data managers.

Open-File Report↗
USGS2024

A regional synthesis of climate data to inform the 2025 State Wildlife Action Plans in the Northeast U.S.

The State Wildlife Action Plans (SWAPs) are proactive planning documents, known as “comprehensive wildlife conservation strategies,” that assess the health of each state’s wildlife and habitats, identify current management and conservation challenges, and outline needed actions to conserve natural resources over the long term. SWAPs are revised every 10 years, with the last revision in 2015 and the next revision anticipated in 2025. State managers have a long history of managing for threats such as land-use change, pollution, and harvest. However, they have expressed a lack of expertise and capacity to keep pace with the rapid advances in climate science and noted that much of the information available is not scaled to meet their needs; thus making the prospect of integrating climate information into SWAPs a daunting task. (Johnson, 2018; Yocum et al., 2021; Blandford, 2022). This report, led by the Northeast Climate Adaptation Science Center (NE CASC), directly addresses SWAP climate science data needs through a Northeast regional synthesis across four key areas of climate science: 1) observed and projected climate changes, 2) species responses to climate change, 3) climate vulnerabilities and risks, and 4) scale-specific adaptation strategies and actions. In addition, case studies of climate adaptation efforts and climate threat to-action narratives provide illustrative examples of how climate change frameworks and tools are being operationalized in decision-making processes related to Regional Species of Greatest Conservation Need (RSGCN) and their habitats across the region. Lists of recent climate resources were also synthesized into extensive data tables to provide SWAP writing teams with a comprehensive platform of information to support content development (AFWA 2022).

Connecticut, Delaware, Maine, Maryland, Massachuse↗
USGS2020

The Modern Geological Survey; a model for research, innovation, synthjesis: A USGS perspective

Geological Surveys have long filled the role of providing Earth system science data and knowledge. These functions are increasingly complicated by accelerating environmental and societal change. Here we describe the USGS response to these evolving conditions. Underpinning the USGS approach is the recognition that many of the issues facing the U.S. and the world involve the interaction among geologic, hydrologic, and biologic processes, and how these interactions in turn affect society. Therefore, a goal of USGS planning is fostering interdisciplinary science. This focus is occurring in part through implementation of the recommendations of strategic planning teams. The USGS has also put in place groups building a broad information technology infrastructure as well as identifying and disseminating new Earth science research tools. In addition, the USGS has established an analysis and synthesis center that brings together groups of scientists who address interdisciplinary Earth system science issues. The goal is for these building blocks to evolve towards a comprehensive USGS data and knowledge platform; EarthMAP (Earth Monitoring, Assessment, and Projection). We also recognize that the modern geological survey must be a member of a community of geological surveys contributing data to a global database of 3-dimensional biogeophysical observations and interpretations.

Book chapter↗
USGS2017

Integrating count and detection–nondetection data to model population dynamics

There is increasing need for methods that integrate multiple data types into a single analytical framework as the spatial and temporal scale of ecological research expands. Current work on this topic primarily focuses on combining capture–recapture data from marked individuals with other data types into integrated population models. Yet, studies of species distributions and trends often rely on data from unmarked individuals across broad scales where local abundance and environmental variables may vary. We present a modeling framework for integrating detection–nondetection and count data into a single analysis to estimate population dynamics, abundance, and individual detection probabilities during sampling. Our dynamic population model assumes that site-specific abundance can change over time according to survival of individuals and gains through reproduction and immigration. The observation process for each data type is modeled by assuming that every individual present at a site has an equal probability of being detected during sampling processes. We examine our modeling approach through a series of simulations illustrating the relative value of count vs. detection–nondetection data under a variety of parameter values and survey configurations. We also provide an empirical example of the model by combining long-term detection–nondetection data (1995–2014) with newly collected count data (2015–2016) from a growing population of Barred Owl ( Strix varia ) in the Pacific Northwest to examine the factors influencing population abundance over time. Our model provides a foundation for incorporating unmarked data within a single framework, even in cases where sampling processes yield different detection probabilities. This approach will be useful for survey design and to researchers interested in incorporating historical or citizen science data into analyses focused on understanding how demographic rates drive population abundance.

Ecology↗
USGS2011

The use of U.S. Geological Survey digital geospatial data products for science research

The development of geographic information system (GIS) transformed the practice of geographic science research. The availability of low-cost, reliable data by the U.S. Geological Survey (USGS) supported the advance of GIS in the early stages of the transition to digital technology. To estimate the extent of the scientific use of USGS digital geospatial data products, a search of science literature databases yielded numbers of articles citing USGS products. Though this method requires careful consideration to avoid false positives, these citation numbers of three types of products (vector, land-use/land-cover, and elevation data) were graphed, and the frequency trends were examined. Trends indicated that the use of several, but not all, products increased with time. The use of some products declined and reasons for these declines are offered. To better understand how these data affected the design and outcomes of research projects, the study begins to build a context for the data by discussing digital cartographic research preceding the production of mass-produced products. The data distribution methods used various media for different system types and were supported by instructional material. The findings are an initial assessment of the affect of USGS products on GIS-enabled science research. A brief examination of the specific papers indicates that USGS data were used for science and GIS conceptual research, advanced education, and problem analysis and solution applications.

Book chapter↗
USGS2013

NW CSC annual report fiscal year 2013

The Northwest Climate Science Center (NW CSC) was established in 2010 as one of eight regional Climate Science Centers created by the Department of the Interior (DOI). The NW CSC encompasses Washing-ton, Oregon, Idaho, and western Montana and has overlapping boundaries with three Landscape Conservation Cooperatives (LCCs): the Great Northern, the Great Basin, and the North Pacific. With guidance from its Executive Stakeholder Advisory Committee (ESAC), the NW CSC and its partner LCCs are addressing the highest priority regional climate science needs of Northwest natural and cultural resource managers. Climate Science Centers tap into the scientific expertise of both the U.S. Geological Survey (USGS) and academic institutions. The NW CSC is supported by an academic consortium with the capacity to generate climate science and tools in a coordinated fashion, serving stakeholders across the Northwest region. This consortium is primarily represented by Oregon State University (OSU), the University of Id-ho (UI), and the University of Washington (UW). The academic consortium and USGS provide capabilities in climate science, ecology, impacts and vulnerability assessment, modeling, adaptation planning, and advanced information technology, all necessary to address and respond to climate change in the Northwest. University members also recruit and train graduate students and early-career scientists. This Annual Report summarizes progress for the goals set out in the NW CSC Strategic Plan for 2012-2015 (http://www.doi.gov/csc/northwest/upload/Northwest-CSC-Strategic-Plan.cfm) and the NW CSC Work-plan for Fiscal Year (FY) 2013 (October 1, 2012 through September 30, 2013). The report follows the structure of the Strategic Plan, which describes the five core services (Executive, Science, Data, Communications, and Education and Training) provided by the NW CSC in support of the stated vision: Our Vision: To become nationally recognized as a best-practice model for the provision of climate science and decision support tools to address conservation and management issues in the Pacific Northwest Region.

Report↗
USGS2019

Assessment of Columbia and Willamette River flood stage on the Columbia Corridor Levee System at Portland, Oregon, in a future climate

To support Levee Ready Columbia’s (LRC’s) effort to re-certify levees along the Columbia and Willamette Rivers and remain accredited, two 2-dimensional hydraulic models, Adaptive Hydraulics and Delft3D-Flexible Mesh, were used to simulate the effects of plausible extreme high water during the 2030 to 2059 period. The Columbia River was simulated from Bonneville Dam, situated at river mile (RM) 145, to the mouth of Columbia River, and the Willamette River was simulated from Willamette Falls, RM 26.2, to the Columbia River confluence. Inputs to the models included light detection and ranging (lidar) and bathymetric mapping data to determine bed level, and boundary conditions in the form of daily inflow hydrographs and water levels in the ocean offshore of the mouth of the Columbia River. Future conditions were based on climate science data developed by the U.S. Army Corps of Engineers and others. These conditions included future streamflow and coastal ocean water levels. The hypothetical, extreme but plausible, upstream boundary was based on scaling up the hydrographs from the 1996 flood. Scaling factors were determined by comparing the peak flow rankings determined from flood frequency analyses of historical unregulated periods and 2040s simulated unregulated winter streamflow. The comparison resulted in scaling up the Columbia River hydrograph by 40-percent and scaling up the Willamette River and Lower Columbia River tributaries hydrographs by 20-percent. The downstream ocean boundary was based on a combination of sea-level change, high tide, and storm surge. The models were calibrated for two historical periods: (1) from January 15 to February 28, 1996, and (2) from April 12 to July 12, 1997. The two models compared well to the measured water-surface elevation over the historical periods and had good performance statistics, with root-mean square error ranging from 0.085 to 0.32 meters, Nash-Sutcliffe values greater than 0.96, and bias ranging from -0.03 to 0.28 meters. The simulated peak stage in the Columbia River at Vancouver, Washington, for 1996 was 9.60 and 9.98 meters (31.5 and 32.7 feet) compared to the measured peak of 9.89 meters (32.5 feet). Future peak stage then was simulated with boundary conditions representing extreme but plausible future conditions at the inflow sites and the ocean boundary. The two calibrated models compared well in their simulations of extreme but plausible future conditions. For the 0-meter sea-level change scenario, the simulated peak stage in the Columbia River at Vancouver was 11.15 and 11.39 meters (36.6 and 37.4 feet); and for the 1-meter sea-level change scenario, the simulated peak stage in the Columbia River was 11.25 and 11.54 meters (36.9 and 37.9 feet). The total increase in stage as compared to the 1996 measured peak stage ranged from 1.26 to 1.65 meters (4.13 to 5.40 feet).

Oregon↗
USGS2022

Living with wildfire in Log Hill Mesa, Ouray County, Colorado: 2017 data report and a comparison to 2011 and 2012 data

Over the last decade, a team of researchers and practitioners, referred to as the Wildfire Research Team (WiRē1 Team), has worked with wildfire practitioners seeking to create communities that are adapted to wildfire through an evidenced-based approach. The West Region Wildfire Council (WRWC) has been an integral partner amongst the WiRē Team throughout this time. Together, the WiRē Team has developed a systematic data collection and integration approach (the WiRē approach) that informs local wildfire risk education efforts and allows for monitoring of community adaptation over time. Through this approach, we collect and analyze locally relevant wildfire risk and social science data to enhance the effectiveness of local wildfire risk mitigation efforts. A unique aspect of this report is that the data collection effort was replicated. The results of the 2011/2012 data collection effort are summarized in Meldrum et. al. (2013). In this report, the results of the 2017 data collection effort are summarized first, followed by a brief comparison of the 2017 results to the 2011/2012 data. This comparison is offered to provide context to the 2017 data; in-depth investigation of change over time is beyond the scope of this report.

Colorado↗
USGS2009

Review of FEWS NET biophysical monitoring requirements

The Famine Early Warning System Network (FEWS NET) provides monitoring and early warning support to decision makers responsible for responding to famine and food insecurity. FEWS NET transforms satellite remote sensing data into rainfall and vegetation information that can be used by these decision makers. The National Aeronautics and Space Administration has recently funded activities to enhance remote sensing inputs to FEWS NET. To elicit Earth observation requirements, a professional review questionnaire was disseminated to FEWS NET expert end-users; it focused upon operational requirements to determine additional useful remote sensing data and, subsequently, to assess whether such data would be beneficial as FEWS NET biophysical supplementary inputs. The review was completed by over 40 experts from around the world. Reviewers were asked to evaluate the relative importance of environmental variables and spatio-temporal requirements for Earth science data products, in particular for rainfall and vegetation products. The results showed that spatio-temporal resolution requirements are complex and need to vary according to place, time, and hazard; that high resolution remote sensing products continue to be in demand; and that rainfall and vegetation products are valued as data that provide actionable food security information.

Environmental Research Letters↗
USGS2006

The MODIS reprojection tool

The MODIS Reprojection Tool (MRT) is designed to help individuals work with MODIS Level-2G, Level-3, and Level-4 land data products. These products are referenced to a global tiling scheme in which each tile is approximately 10° latitude by 10° longitude and non-overlapping (Fig. 9.1). If desired, the user may reproject only selected portions of the product (spatial or parameter subsetting). The software may also be used to convert MODIS products to file formats (generic binary and GeoTIFF) that are more readily compatible with existing software packages. The MODIS land products distributed by the Land Processes Distributed Active Archive Center (LP DAAC) are in the Hierarchical Data Format - Earth Observing System (HDF-EOS), developed by the National Center for Supercomputing Applications at the University of Illinois at Urbana Champaign for the NASA EOS Program. Each HDF-EOS file is comprised of one or more science data sets (SDSs) corresponding to geophysical or biophysical parameters. Metadata are embedded in the HDF file as well as contained in a .met file that is associated with each HDF-EOS file. The MRT supports 8-bit, 16-bit, and 32-bit integer data (both signed and unsigned), as well as 32-bit float data. The data type of the output is the same as the data type of each corresponding input SDS.

Book chapter↗
USGS2023

The value of scientific information — An overview

Introduction The U.S. Geological Survey (USGS) provides reliable science, data, information, and models (hereafter collectively referred to as “information”) to describe and understand the Earth. This information is used to minimize loss of life and property from natural disasters; manage water, biological, energy, and mineral resources; and enhance and protect quality of life. USGS science informs public and private decisions, operations, and risk management in all major United States economic sectors, as defined by the Bureau of Economic Analysis, and provides critical information for natural resource and natural hazard management and stewardship decisions. Understanding the value of scientific information supports applications of USGS science in land- and water-management decisions, and better informs the public about the return on investment of USGS programs. USGS economists, social scientists, and physical scientists are engaged in collaborative efforts to advance methods to estimate the value of information (VOI) produced by the USGS. These efforts involve collaborating with an international community to develop and refine estimation methods, establish best practices to determine VOI, develop a study repository, and conduct projects to assess the VOI of specific information products and their application. This report focuses on economic valuation conducted by USGS specifically, although the methodology has much broader applicability within the U.S. government, academia, and beyond. Noneconomic valuation techniques for assessing the VOI also exist but are not addressed in this report.

Open-File Report↗
USGS2017

A land data assimilation system for sub-Saharan Africa food and water security applications

Seasonal agricultural drought monitoring systems, which rely on satellite remote sensing and land surface models (LSMs), are important for disaster risk reduction and famine early warning. These systems require the best available weather inputs, as well as a long-term historical record to contextualize current observations. This article introduces the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS), a custom instance of the NASA Land Information System (LIS) framework. The FLDAS is routinely used to produce multi-model and multi-forcing estimates of hydro-climate states and fluxes over semi-arid, food insecure regions of Africa. These modeled data and derived products, like soil moisture percentiles and water availability, were designed and are currently used to complement FEWS NET’s operational remotely sensed rainfall, evapotranspiration, and vegetation observations. The 30+ years of monthly outputs from the FLDAS simulations are publicly available from the NASA Goddard Earth Science Data and Information Services Center (GES DISC) and recommended for use in hydroclimate studies, early warning applications, and by agro-meteorological scientists in Eastern, Southern, and Western Africa.

Scientific Data↗
USGS2018

Earth as art 5

Fanciful Fluorescence. Lurking Madness. Serene Expressions. The titles of the images in this fifth edition of Earth As Art speak to the powerfully artistic qualities of Earth’s natural features when tinged with unnatural colors. Art serves as a great partner in the communication of science, bringing emotion to the pursuit of understanding. The pieces in this collection look like abstract art but are actual satellite images. Satellite imagery has long served the rational and disciplined approaches of science to better understand our Earth. But these images can also, with a bit of creativity, excite our imaginations with the beauty and art that surround us. In this newest collection of Earth As Art, we continue to display the Earth as our eyes cannot see it—in creative combinations of visible and infrared light. Although beauty in art is often subjective, the science data provide objective views of the Earth’s changing land surface. However, we will let these images speak to you as art. Enjoy the latest additions to Earth As Art! The images in the Earth As Art 5 collection can be downloaded for free from the Earth Resources Observation and Science (EROS) Center Image Gallery at https://www.usgs.gov/centers/eros/science/earth-art-5 .

General Information Product↗
USGS2002

Operating the EOSDIS at the land processes DAAC managing expectations, requirements, and performance across agencies, missions, instruments, systems, and user communities

NASA developed the Earth Observing System (EOS) during the 1990'S. At the Land Processes Distributed Active Archive Center (LP DAAC), located at the USGS EROS Data Center, the EOS Data and Information System (EOSDIS) is required to support heritage missions as well as Landsat 7, Terra, and Aqua. The original system concept of the early 1990'S changed as each community had its say - first the managers, then engineers, scientists, developers, operators, and then finally the general public. The systems at the LP DAAC - particularly the largest single system, the EOSDIS Core System (ECS) - are changing as experience accumulates, technology changes, and each user group gains influence. The LP DAAC has adapted as contingencies were planned for, requirements and therefore plans were modified, and expectations changed faster than requirements could hope to be satisfied. Although not responsible for Quality Assurance of the science data, the LP DAAC works to ensure the data are accessible and useable by influencing systems, capabilities, and data formats where possible, and providing tools and user support as necessary. While supporting multiple missions and instruments, the LP DAAC also works with and learns from multiple management and oversight groups as they review mission requirements, system capabilities, and the overall operation of the LP DAAC. Stakeholders, including the Land Science community, are consulted regularly to ensure that the LP DAAC remains cognizant and responsive to the evolving needs of the user community. Today, the systems do not look or function as originally planned, but they do work, and they allow customers to search and order of an impressive amount of diverse data.

Conference Paper↗
USGS2017

Grand challenges for integrated USGS science — A workshop report

Executive Summary The U.S. Geological Survey (USGS) has a long history of advancing the traditional Earth science disciplines and identifying opportunities to integrate USGS science across disciplines to address complex societal problems. The USGS science strategy for 2007–2017 laid out key challenges in disciplinary and interdisciplinary arenas, culminating in a call for increased focus on a number of crosscutting science directions. Ten years on, to further the goal of integrated science and at the request of the Executive Leadership Team (ELT), a workshop with three dozen invited scientists spanning different disciplines and career stages in the Bureau convened on February 7–10, 2017, at the USGS John Wesley Powell Center for Analysis and Synthesis in Fort Collins, Colorado. The workshop focused on identifying “grand challenges” for integrated USGS science. Individual participants identified nearly 70 potential grand challenges before the workshop and through workshop discussions. After discussion, four overarching grand challenges emerged: Natural resource security, Societal risk from existing and emerging threats, Smart infrastructure development, and Anticipatory science for changing landscapes. Participants also identified a “comprehensive science challenge” that highlights the development of integrative science, data, models, and tools—all interacting in a modular framework—that can be used to address these and other future grand challenges: Earth Monitoring, Analyses, and Projections (EarthMAP) EarthMAP is our long-term vision for an integrated scientific framework that spans traditional scientific boundaries and disciplines, and integrates the full portfolio of USGS science: research, monitoring, assessment, analysis, and information delivery. The Department of Interior, and the Nation in general, have a vast array of information needs. The USGS meets these needs by having a broadly trained and agile scientific workforce. Encouraging and supporting cross-discipline engagement would position the USGS to tackle complex and multifaceted scientific and societal challenges in the 21st Century.

Open-File Report↗
USGS2022

Predicting geothermal favorability in the western United States by using machine learning: Addressing challenges and developing solutions

Previous moderate- and high-temperature geothermal resource assessments of the western United States utilized weight-of-evidence and logistic regression methods to estimate resource favorability, but these analyses relied upon some expert decisions. While expert decisions can add confidence to aspects of the modeling process by ensuring only reasonable models are employed, expert decisions also introduce human bias into assessments. This bias presents a source of error that may affect the performance of the models and resulting resource estimates. Our study aims to reduce expert input through robust data-driven analyses and better-suited data science techniques, with the goals of saving time, reducing bias, and improving predictive ability. We present six favorability maps for geothermal resources in the western United States created using two strategies applied to three modern machine learning algorithms (logistic regression, support-vector machines, and XGBoost). To provide a direct comparison to previous assessments, we use the same input data as the 2008 U.S. Geological Survey (USGS) conventional moderate- to high-temperature geothermal resource assessment. The six new favorability maps required far less expert decision-making, but broadly agree with the previous assessment. Despite the fact that the 2008 assessment results employed linear methods, the non-linear machine learning algorithms (i.e., support-vector machines and XGBoost) produced greater agreement with the previous assessment than the linear machine learning algorithm (i.e., logistic regression). It is not surprising that geothermal systems depend on non-linear combinations of features, and we postulate that the expert decisions during the 2008 assessment accounted for system non-linearities. Substantial challenges to applying machine learning algorithms to predict geothermal resource favorability include severe class imbalance (i.e., there are very few known geothermal systems compared to the large area considered), and while there are known geothermal systems (i.e., positive labels), all other sites have an unknown status (i.e., they are unlabeled), instead of receiving a negative label (i.e., the known/proven absence of a geothermal resource). We address both challenges through a custom undersampling strategy that can be used with any algorithm and then evaluated using F1 scores.

western United States↗
USGS2026

Anthropogenic and geogenic factors affecting groundwater arsenic and potential drinking water exposure in the San Luis Valley, Colorado

Groundwater arsenic is a serious health threat in many regions with limited surface water drinking supplies. In the San Luis Valley, Colorado, USA, where most residents rely on groundwater for drinking water, elevated groundwater arsenic concentrations (≥5 μg/L) were observed in 19.8% of private wells. Many recent studies have leveraged data science methods to estimate the spatial variability of arsenic at local, regional, and global scales. These studies typically predict depth-independent arsenic concentrations due to limited well depth information. However, arsenic concentrations may vary greatly with depth due to heterogeneous aquifer geochemistry, including redox potential, pH, and biogeochemical conditions. In this study, we apply random forest modeling to predict elevated groundwater arsenic concentrations (≥5 μg/L) over space and depth continuously across the San Luis Valley using a community-driven data set of 435 groundwater measurements of major ions and trace metals. Our model reveals the influence of several known or hypothesized drivers or proxy drivers of arsenic in groundwater, including surface elevation, fraction of fine-grained aquifer sediments, well depth, soil pH, long-term subsidence, and geothermal influences. Elevation, percentage of fine-grained sediments, and elemental indicators of geothermal fluid influence are some of the most important predictors of arsenic, while long-term subsidence is found to have a weak but positive relationship with arsenic. The model has a specificity of 0.82 and a sensitivity of 0.81 on held-out test data sets. In addition to improving understanding of drivers of arsenic in the region, this model produces pseudo-3D estimates of groundwater arsenic. The model can also be used to assess the time-dependent exposure to arsenic as the depth of domestic wells changes over time.

Colorado↗
USGS2026

The digital archivist: Automating legacy macroseismic data processing using large language models

Macroseismic data are a key resource to investigate shaking and damage from preinstrumental and early instrumental eras. However, data are often stored as inconsistently formatted reports describing observed shaking and damage, making manually parsing and interpreting accounts labor‐intensive. We introduce a novel workflow using Google’s Gemini 2.5 Pro large language model (LLM) to automate the extraction and structuring of macroseismic observations from summary reports. We apply this workflow to the 22 March 1957 M 5.3 Daly City, California, earthquake as a case study. We used Gemini to extract addresses, originally assigned modified Mercalli intensity values, and descriptions from each report. To address coordinate precision limits, addresses were geocoded via Google’s Geocoding application programming interface. This workflow yielded over 2300 geocoded intensity reports for the Daly City earthquake. We use the geocoded accounts, with the original report intensity assignments, to develop a shaking intensity map that in some respects rivals modern Did You Feel It? Maps. We also extract and present data for the 9 February 1971 M L 6.7 Sylmar, California, earthquake. Our results demonstrate the potential of LLMs for reliably extracting and analyzing large, unstructured macroseismic datasets. LLMs offer a scalable solution for rapidly digitizing macroseismic archives, enabling their broader use to constrain ground‐motion models in modern seismic hazard analysis and to improve our understanding of site effects in urban areas. The concepts explored here may also be applied to the handling of other legacy seismological and earth science data.

Seismological Research Letters↗