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Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

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At least 973 records · Page 54Linked to original sources

USGS2017

Nutrients, phytoplankton, zooplankton, and macrobenthos

Lower trophic levels support the prey fish on which most sport fish depend. Therefore, understanding the production potential of lower trophic levels is integral to the management of Lake Ontario’s fishery resources. Lower trophic-level productivity differs among offshore and nearshore waters. In the offshore, there is concern about the ability of the lake to support Alewife (Table 1) production due to a perceived decline in productivity of phytoplankton and zooplankton whereas, in the nearshore, there is a concern about excessive attached algal production (e.g., Cladophora) associated with higher nutrient concentrations—the oligotrophication of the offshore and the eutrophication of the nearshore (Mills et al. 2003; Holeck et al. 2008; Dove 2009; Koops et al. 2015; Stewart et al. 2016). Even though the collapse of the Alewife population in Lake Huron in 2003 (and the associated decline in the Chinook Salmon fishery) may have been precipitated by a cold winter (Dunlop and Riley 2013), Alewife had not returned to high abundances in Lake Huron as of 2014 (Roseman et al. 2015). Failure of the Alewife population to recover from collapse has been attributed to declines in lower trophic-level production (Barbiero et al. 2011; Bunnell et al. 2014; but see He et al. 2015). In Lake Michigan, concerns of a similar Alewife collapse led to a decrease in the number of Chinook Salmon stocked. If lower trophic-level production declines in Lake Ontario, a similar management action could be considered. On the other hand, in Lake Erie, which supplies most of the water in Lake Ontario, eutrophication is increasing and so are harmful algal blooms. Thus, there is also a concern that nutrient levels and algal blooms could increase in Lake Ontario, especially in the nearshore. Solutions to the two processes of concern—eutrophication in the nearshore and oligotrophication in the offshore—may be mutually exclusive. In either circumstance, fisheries management needs information on the productivity of lower trophic levels in Lake Ontario. In this chapter, we review the status of lower trophic levels in Lake Ontario with special attention to the current (2008-2013) and previous (2003-2007) reporting periods. During the two reporting periods, three whole-lake surveys of lower trophic levels were conducted: the Lower Trophic Level Assessment (LOLA) in 2003 and 2008 (Makarewicz and Howell 2012; Munawar et al. 2015b) and the Cooperative Science and Management Initiative (CSMI) in 2013. Analyses of the CSMI data are ongoing. In addition to the three one-year sources of information on lower trophic levels, several multi-year sources of information are available, including data from the surveillance program conducted since 1965 by Environment Canada (EC) (Dove 2009), monitoring conducted since 1980 by the U.S. Environmental Protection Agency’s (EPA) Great Lakes National Program Office (GLNPO) (Barbiero et al. 2014; Reavie et al. 2014), sampling for a Bioindex Program at two stations, one offshore and one in the Eastern Basin, assessments of Mysis diluviana (formerly Mysis relicta) conducted since 1980 by Fisheries and Oceans Canada (Johannsson et al. 1998, 2011) and the Ontario Ministry of Natural Resources and Forestry (OMNRF), and monitoring conducted since 1995 by the Biomonitoring Program (BMP) on the New York side of the lake (Holeck et al. 2015b). The BMP is a collaboration of the New York State Department of Environmental Conservation (DEC), U.S. Fish and Wildlife Service, U.S. Geological Survey (USGS), and Cornell University.

Special Publication↗

USGS2010

Geophysical Data Collected off the South Shore of Martha's Vineyard, Massachusetts

The U.S. Geological Survey Woods Hole Science Center conducted a nearshore geophysical survey offshore of the southern coast of Martha's Vineyard, in the vicinity of the Martha's Vineyard Coastal Observatory in 2007. This mapping program was part of a larger research effort supporting the Office of Naval Research Ripples Directed-Research Initiative studies at Martha's Vineyard Coastal Observatory designed to improve our understanding of coastal sediment-transport processes. The survey was conducted aboard the Megan T. Miller August 9-13, 2007. The study area covers 35 square kilometers from about 0.2 kilometers to 5 kilometers offshore of the south shore of Martha's Vineyard, and ranges in depth from ~6 to 24 meters. The geophysical mapping utilized the following suite of high-resolution instrumentation to map the surficial sediment distribution, bathymetry, and sub-surface geology: a dual-frequency 100/500 kilohertz sidescan-sonar system, 234 kilohertz interferometric sonar, and 500 hertz -12 kilohertz chirp subbottom profiler. These geophysical data will be used to provide initial conditions for wave and circulation modeling within the study area.

Open-File Report↗

USGS2020

USGS “Did You Feel It?” — Science and lessons from twenty years of citizen science-based macroseismology

The U.S. Geological Survey (USGS) “Did You Feel It?” (DYFI) system is an automatic method for rapidly collecting macroseismic intensity data from Internet users’ shaking and damage reports and for generating intensity maps immediately following felt earthquakes. DYFI has been in operation for nearly two decades (1999-2019) in the United States, and for nearly 15 years globally. During that period the amount of data collected is astounding: Over 5 million individual DYFI intensity reports—spanning all magnitude and distance ranges—have been amassed and archived. DYFI allows for macroseismic data collection at rates and quantities never before imagined, and thus high-quality macroseismic intensity maps can be made almost immediately, and with more complete coverage at higher resolution than in the past. DYFI also allows for valuable positive interactions of the citizenry with a Federal science agency. In essence, the widespread adoption of DYFI—along with ShakeMap—has facilitated the general acceptance of the very concept of shaking intensity, fundamentally improving our agency’s ability to communicate both hazard and risk to the population. DYFI effectively confirms the importance of reporting and inculcating the public’s understanding of intensity—in addition to magnitude—for a proper perspective of earthquake risk-related decision-making. Furthermore, the vast amount of DYFI data allow for data-rich analyses of otherwise intractable seismological, sociological, and earthquake impact studies that range from quantifying the shaking due to induced earthquakes, to human response and risk perception, to relating recorded shaking metrics to macroseismic effects, and the attenuation of intensity with magnitude and distance. Naturally, web-based data collection also poses challenges. After two decades of experience acquiring data with the DYFI system, we address some of these hallenges by documenting refinements to our algorithmic and operational procedures that have evolved over that time. Lastly, we outline new opportune research and development directions for our DYFI approach to citizen seismology.

Frontiers in Earth Science↗

USGS2007

Rapid Data Delivery System (RDDS)

Since the start of the active 2000 summer fire season, the U. S. Geological Survey (USGS) Rocky Mountain Geographic Science Center (RMGSC) has been actively engaged in providing crucial and timely support to Federal, State, and local natural hazards monitoring, analysis, response, and recovery activities. As part of this support, RMGSC has developed the Rapid Data Delivery System (RDDS) to provide emergency and incident response teams with timely access to geospatial data. The RDDS meets these needs by combining a simple web-enabled data viewer for the selection and preview of vector and raster geospatial data with an easy to use data ordering form. The RDDS viewer also incorporates geospatial locations for current natural hazard incidents, including wildfires, earthquakes, hurricanes, and volcanoes, allowing incident responders to quickly focus on their area of interest for data selection.

Fact Sheet↗

USGS2012

Archive of post-Hurricane Charley coastal oblique aerial photographs collected during U.S. Geological Survey field activity 04CCH01 from Marco Island to Fort DeSoto, Florida, August 15, 2004

On August 15, 2004, the U.S. Geological Survey (USGS) conducted an oblique aerial photographic survey off the southwest coast of Florida, from Marco Island to Fort DeSoto, aboard a Navajo Chieftain airplane, tail number N2KK, at an altitude of 500 ft and approximately 1000 ft offshore. These photographs were used to document coastal changes such as beach erosion and overwash caused by Hurricane Charley. They will also be used as baseline data for future coastal change. The oblique photography also served as qualitative ground truthing for the Experimental Advanced Airborne Research Lidar (EAARL) coastal topography and bathymetry data collected on August 16, 2004 (Bonisteel and others, 2009). This report serves as an archive of photographs collected during the August 15, 2004, post-Hurricane Charley coastal oblique aerial survey along with associated flight path maps, KML files, navigation files, digital Field Activity Collection System (FACS) logs, and Federal Geographic Data Committee (FGDC) metadata. Refer to the Acronyms page for expansions of all acronyms and abbreviations used in this report. The USGS St. Petersburg Coastal and Marine Science Center (SPCMSC) assigns a unique identifier to each cruise or field activity. For example, 04CCH01 tells us the data were collected in 2004 for the Coastal Change Hazards (CCH) study and the data were collected during the first field activity for that project in that calendar year. Refer to http://walrus.wr.usgs.gov/infobank/programs/html/definition/activity.html for a detailed description of the method used to assign the ID number. Two separate records of flight navigation were collected during the survey. The first was a continuous ASCII text file from the PLGR that recorded only latitudes, longitudes, and altitudes every 30 sec for the entire flight. No time values were recorded by the PLGR. The second navigation record was recorded by a Trimble Centurion GPS and converted to subtitles on the video, using a Compix Titler unit. The video was shot continuously during the survey. The video subtitles recorded day, month, year, latitude, longitude, and time in hours, minutes, and seconds. In order to produce a digital record of the navigation values that included latitude, longitude, and time, each was manually extracted from the video every 5 min, and these values were matched to the latitude and longitude in the PLGR file. Next, the time was interpolated between these 5-min fixes using Excel to produce time values for each navigation fix recorded in the PLGR file. The location of each photograph taken was determined in the following manner. A Nikon MF-14 data back marks the time each photograph was acquired on the lower right corner of the image in day, hour, and minute format (in UTC). These values were entered from the photographs into an Excel spreadsheet. It is assumed for the purposes of locating the images that the photographs were taken at a constant rate during any given minute of flight. To assign the time value in seconds to each photograph, the number of photographs taken during each minute was evenly distributed across that minute. For example, if 15 photographs were taken during minute 19:00:00, we assume that a picture was taken every 4 sec. The photographs were assigned the time values 19:00:00, 19:00:04, 19:00:08, and so on. The video time navigation file was then merged with the new interpolated photograph file based on time to produce the point of which each photograph was collected. As a result, the positions assigned to each photograph are an estimate of the aircraft position, not the location of the landmark photographed. The photographs provided here are JPEG scanned images of the analog slides. The metadata values for photo creation time, GPS latitude, GPS longitude, GPS position, keywords, credit, artist, caption, copyright, and contact were added to each photograph's EXIF header using EXIFtools (see the Software page). Photographs can be opened directly with any JPEG-compatible image viewer by clicking on a thumbnail on the contact sheet, or, when viewing the Google Earth KML file, by clicking on the marker and then clicking on either the thumbnail or the link below the thumbnail. The KML files were created using the photographic navigation files and the CreateKML JavaScript (see the Software page). To view the survey maps and navigation files, and for more information about these items, see the Navigation page. Figure 1 displays the acquisition geometry. The tables provide detailed information about the assigned location, name, data, and time the photograph was taken along with links to the photo and corresponding 5-min contact sheet. Refer to table 1 and table 2 for details of the northern and southern county photographs, respectively.

Florida↗

USGS2013

Archive of post-Hurricane Isabel coastal oblique aerial photographs collected during U.S. Geological Survey Field Activity 03CCH01 from Ocean City, Maryland, to Fort Caswell, North Carolina and Inland from Waynesboro to Redwood, Virginia, September 21 - 23, 2003

On September 21 - 23, 2003, the United States Geological Survey (USGS) conducted an oblique aerial photographic survey along the Atlantic coast from Ocean City, Md., to Fort Caswell, N.C., and inland oblique aerial photographic survey from Waynesboro to Redwood, Va., aboard a Navajo Piper twin-engine airplane. The coastal survey was conducted at an altitude of 500 feet (ft) and approximately 1,000 ft offshore. For the inland photos, the aircraft tried to stay approximately 500 ft above the terrain. These coastal photos were used to document coastal changes like beach erosion and overwash caused by Hurricane Isabel, while the inland photos looked for potential landslides caused by heavy rains. The photos may also be used as baseline data for future coastal change analysis. The USGS and the National Aeronautics and Space Administration (NASA) surveyed the impact zone of Hurricane Isabel to better understand the changes in vulnerability of the Nation’s coasts to extreme storms (Morgan, 2009). This report serves as an archive of photographs collected during the September 21 - 23, 2003, post-Hurricane Isabel coastal and inland oblique aerial survey along with associated survey maps, KML files, navigation files, digital Field Activity Collection System (FACS) logs, and Federal Geographic Data Committee (FGDC) metadata. Refer to the Acronyms page for expansions of all acronyms and abbreviations used in this report. The USGS St. Petersburg Coastal and Marine Science Center (SPCMSC) assigns a unique identifier to each cruise or field activity. For example, 03CCH01 tells us the data were collected in 2003 for the Coastal Change Hazards (CCH) study and the data were collected during the first field activity for that project in that calendar year. Refer to http://walrus.wr.usgs.gov/infobank/programs/html/definition/activity.html for a detailed description of the method used to assign the ID number. The photographs provided here are Joint Photographic Experts Group (JPEG) scanned images of the analog 35 millimeter (mm) color positive slides. The photograph locations are estimates of the location of the plane (see the Navigation page). The metadata values for photo creation time, GPS latitude, GPS longitude, GPS position (latitude and longitude), keywords, credit, artist, caption, copyright, and contact were added to each photograph's EXIF header using EXIFtool (Subino and others, 2012). Photographs can be opened directly with any JPEG-compatible image viewer by clicking on a thumbnail on the contact sheet, or, when viewing the Google Earth KML file, by clicking on the marker and then clicking on either the thumbnail or the link below the thumbnail. Nathaniel Plant (USGS - St. Petersburg, Fla.), and Ann Marie Ascough (formerly contracted at the USGS - St. Petersburg, Fla.) helped with the creation of KML files. To view the photos and survey maps, proceed to the Photos and Maps page.

Maryland, North Carolina, Virginia↗

USGS2014

Sediment data collected in 2010 from Cat Island, Mississippi

Scientists from the U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center, in collaboration with the U.S. Army Corps of Engineers, conducted geophysical and sedimentological surveys in 2010 around Cat Island, Mississippi, which is the westernmost island in the Mississippi-Alabama barrier island chain. The objective of the study was to understand the geologic evolution of Cat Island relative to other barrier islands in the northern Gulf of Mexico by identifying relationships between the geologic history, present day morphology, and sediment distribution. This data series serves as an archive of terrestrial and marine sediment vibracores collected August 4-6 and October 20-22, 2010, respectively. Geographic information system data products include marine and terrestrial core locations and 2007 shoreline data. Additional files include marine and terrestrial core description logs, core photos, results of sediment grain-size analyses, optically stimulated luminescence dating and carbon-14 dating locations and results, Field Activity Collection System logs, and formal Federal Geographic Data Committee metadata.

Mississippi↗

USGS2018

A guide to processing bat acoustic data for the North American Bat Monitoring Program (NABat)

The North American Bat Monitoring Program (NABat) aims to improve the state of conservation science for all species of bats shared by the United States, Canada, and Mexico. To accomplish this goal, NABat offers guidance and standardized protocols for acoustic monitoring of bats. In this document, “A Guide to Processing Bat Acoustic Data for the North American Bat Monitoring Program (NABat),” we provide general recommendations and specific workflows for the process of identifying bat species from acoustic files recorded using the NABat stationary point and mobile transect acoustic monitoring protocols.

Open-File Report↗

USGS2012

Archive of single beam and swath bathymetry data collected nearshore of the Gulf Islands National Seashore, Mississippi, from West Ship Island, Mississippi, to Dauphin Island, Alabama: Methods and data report for USGS Cruises 08CCT01 and 08CCT02, July 2008, and 09CCT03 and 09CCT04, June 2009

During the summers of 2008 and 2009 the USGS conducted bathymetric surveys from West Ship Island, Miss., to Dauphin Island, Ala., as part of the Northern Gulf of Mexico (NGOM) Ecosystem Change and Hazard Susceptibility project. The survey area extended from the shoreline out to approximately 2 kilometers and included the adjacent passes (fig. 1). The bathymetry was primarily used to create a topo-bathymetric map and provide a base-level assessment of the seafloor following the 2005 hurricane season. Additionally, these data will be used in conjunction with other geophysical data (chirp and side scan sonar) to construct a comprehensive geological framework of the Mississippi Barrier Island Complex. The culmination of the geophysical surveys will provide baseline bathymetry necessary for scientists to define and interpret seafloor habitat for this area and for scientists to predict future geomorpholocial changes of the islands with respect to climate change, storm impact, and sea-level rise. Furthermore, these data provide information for feasibility of barrier island restoration, particularly in Camille Cut, and for the preservation of historical Fort Massachusetts. For more information refer to http://ngom.usgs.gov/gomsc/mscip/index.html. Since bathymetric surveys have often been conducted for navigational purposes, soundings have traditionally been referenced to a water level datum using tide gages and tide models. Bathymetric measurements referenced to a Global Positioning System (GPS) is a more accurate way of representing water depth and has been implemented in the acquisition and processing procedures for these datasets. Previous single-beam bathymetric studies performed at the USGS Center for Coastal and Marine Science have successfully referenced bathymetric measurements to GPS (DeWitt and others, 2007; Hansen 2008 and 2009). The 2008-2009 bathymetry surveys were conducted as a test to (1) develop acquisition and processing technology utilizing both single beam and swath bathymetry survey methods together, (2) reference both types of measurements to GPS rather than water level, and (3) compare the differences between methods in acquisition and processing. Results of the survey are explained in greater detail within this report. To acquire suitable coverage of the study area in a limited time frame, the seafloor-elevation survey was conducted using three techniques: single-beam bathymetry, interferometric swath bathymetry, and a walking kinematic survey of the island shorelines. All three techniques utilized GPS measurements. Implementation of these techniques was executed concurrently yet independently aboard two research vessels: the RV Survey Cat , a 26-foot (ft) shallow-draft Glacier Bay Coastal Runner, and the 50-ft RV G.K. Gilbert . A portable push buggy with a rigid antenna mount served as the platform for the kinematic shoreline survey. Data from each survey technique was post-processed and edited independently with proper inclusion of the differentially processed external navigation files. The x,y,z components from each method were then combined and the two survey years (2008 and 2009) were merged into one dataset. The 2008 bathymetry data were processed at the USGS Center for Coastal and Marine Science in St. Petersburg, Fla., and the 2009 bathymetry data were processed at the USGS Coastal and Marine Science Center located in Woods Hole, Mass. This report serves as an archive of the processed single beam and interferometric swath bathymetry, outlines the methodology, and reports the results. Data products herein include gridded and interpolated digital depth surfaces, and x,y,z data products for both single beam and interferometric swath bathymetry. Additional files include trackline maps, navigation files, geographic information system (GIS) files, Field Activity Collection System (FACS) logs, and formal Federal Geographic Data Committee (FGDC) metadata. Scanned images of the handwritten FACS logs and digital FACS logs are also provided as PDF files. Refer to the Acronyms page for description of acronyms and abbreviations used in this report or hold the cursor over an acronym for a pop-up explanation. The USGS St. Petersburg Coastal and Marine Science Center assigns a unique identifier to each cruise or field activity. For example, 08CCT01 indicates that the data were collected in 2008 for the Coastal Change and Transport (CCT) study and the data were collected during the first (01) field activity for that project in that calendar year. Refer to http://walrus.wr.usgs.gov/infobank/programs/html/definition/activity.html for a detailed description of the method used to assign the field activity ID. See the digital FACS equipment log for details about the acquisition equipment used. Raw datasets are stored digitally at the USGS St. Petersburg Coastal and Marine Science Center and processed systematically using Novatel's GrafNav version 7.6, SANDS version 3.7, SEA SWATH plus version 3.06.04.03, CARIS HIPS AND SIPS version 3.6, and ESRI ArcGIS version 9.3.1. For more information on processing refer to the Equipment and Processing page. Chirp seismic data were also collected during these surveys and are archived separately.

Mississippi;Alabama↗

USGS2000

Geochemical modeling of iron, sulfur, oxygen and carbon in a coastal plain aquifer

Fe(III) reduction in the Magothy aquifer of Long Island, NY, results in high dissolved-iron concentrations that degrade water quality. Geochemical modeling was used to constrain iron-related geochemical processes and redox zonation along a flow path. The observed increase in dissolved inorganic carbon is consistent with the oxidation of sedimentary organic matter coupled to the reduction of O2 and SO4/2- in the aerobic zone, and to the reduction of SO4/2- in the anaerobic zone; estimated rates of CO2 production through reduction of Fe(III) were relatively minor by comparison. The rates of CO2 production calculated from dissolved inorganic carbon mass transfer (2.55 x 10-4 to 48.6 x 10-4 mmol 1-1 yr-1) generally were comparable to the calculated rates of CO2 production by the combined reduction of O2, Fe(III) and SO4/2- (1.31 x 10-4 to 15 x 10-4 mmol 1-1 yr-1). The overall increase in SO4/2- concentrations along the flow path, together with the results of mass-balance calculations, and variations in ??34S values along the flow path indicate that SO4/2- loss through microbial reduction is exceeded by SO4/2- gain through diffusion from sediments and through the oxidation of FeS2. Geochemichal and microbial data on cores indicate that Fe(III) oxyhydroxide coatings on sediment grains in local, organic carbon- and SO4/2- -rich zones have localized SO4/2- -reducing zones in which the formation of iron disulfides been depleted by microbial reduction and resulted in decreases dissolved iron concentrations. These localized zones of SO4/2- reduction, which are important for assessing zones of low dissolved iron for water-supply development, could be overlooked by aquifer studies that rely only on groundwater data from well-water samples for geochemical modeling. (C) 2000 Elsevier Science B.V.Fe(III) reduction in the Magothy aquifer of Long Island, NY, results in high dissolved-iron concentrations that degrade water quality. Geochemical modeling was used to constrain iron-related geochemical processes and redox zonation along a flow path. The observed increase in dissolved inorganic carbon is consistent with the oxidation of sedimentary organic matter coupled to the reduction of O2 and SO42- in the aerobic zone, and to the reduction of SO42- in the anaerobic zone; estimated rates of CO2 production through reduction of Fe(III) were relatively minor by comparison. The rates of CO2 production calculated from dissolved inorganic carbon mass transfer (2.55??10-4 to 48.6??10-4mmol l-1yr-1) generally were comparable to the calculated rates of CO2 production by the combined reduction of O2, Fe(III) and SO42- (1.31??10-4 to 15??10-4mmol l-1yr-1). The overall increase in SO42- concentrations along the flow path, together with the results of mass-balance calculations, and variations in ??34S values along the flow path indicate that SO42- loss through microbial reduction is exceeded by SO42- gain through diffusion from sediments and through the oxidation of FeS2. Geochemical and microbial data on cores indicate that Fe(III) oxyhydroxide coatings on sediment grains in local, organic carbon- and SO42--rich zones have been depleted by microbial reduction and resulted in localized SO42--reducing zones in which the formation of iron disulfides decreases dissolved iron concentrations. These localized zones of SO42- reduction, which are important for assessing zones of low dissolved iron for water-supply development, could be overlooked by aquifer studies that rely only on groundwater data from well-water samples for geochemical modeling.

Journal of Hydrology↗

USGS2022

Integrating distance sampling survey data with population indices to separate trends in abundance and temporary immigration

Managers rely on accurate estimators of wildlife abundance and trends for management decisions. Despite the focus of contemporary wildlife science on developing methods to improve inference from wildlife surveys, legacy datasets often rely on index counts that lack information about the detection process. Data integration can be a useful tool for combining index counts with data collected under more rigorous designs (i.e., designs that account for the detection process), but care is required when datasets represent different population processes or are mismatched in space and time. This can be particularly problematic in cases where animals aggregate in response to a spatially or temporally limited resource because individuals may temporarily immigrate from outside the study area and be included in the abundance index. Abundance indices based on brown bear ( Ursus arctos ) feeding aggregations within coastal meadows in early summer in Lake Clark National Park and Preserve, Alaska, USA, are one such example. These indices reflect the target population (brown bears residing within the park) and temporary immigrants (i.e., bears drawn from outside the park boundary). To properly account for the effects of temporary immigration, we integrated the index data with abundance data collected via park-wide distance sampling surveys, the latter of which properly addressed the detection process. By assuming that the distance data provide inference on abundance and the index counts represent some combination of abundance and temporary immigration processes, we were able to decompose the relative contribution of each to overall trend. We estimated that the density of brown bears within our study area was 38–54 adults/1,000 km 2 during 2003–2019 and that abundance increased at a rate of approximately 1.4%/year. The contribution of temporary immigrants to overall trend in the index was low, so we created 3 hypothetical scenarios to more fully demonstrate how the integrated approach could be useful in situations where the composite trend in meadow counts may obscure trends in abundance (e.g., opposing trends in abundance and temporary immigration). Our work represents a conceptual advance supporting the integration of legacy index data with more rigorous data streams and is broadly applicable in cases where trends in index values may represent a mixture of population processes.

Alaska↗

USGS2013

Data management challenges in species distribution modeling

An important component in the fields of ecology and conservation biology is understanding the environmental conditions and geographic areas that are suitable for a given species to inhabit. A common tool in determining such areas is species distribution modeling which uses computer algorithms to determine the spatial distribution of organisms. Most commonly the correlative relationships between the organism and environmental variables are the primary consideration. The data requirements for this type of modeling consist of known presence and possibly absence locations of the species as well as the values of environmental or climatic covariates thought to define the species habitat suitability at these locations. These covariate data are generally extracted from remotely sensed imagery, interpolated/gridded historical climate data, or downscaled climate model output. Traditionally, ecologists and biologists have constructed species distribution models using workflows and data that reside primarily on their local workstations or networks. This workflow is becoming challenging as scientists increasingly try to use these modeling techniques to inform management decisions under different climate change scenarios. This challenge stems from the fact that remote sensing products, gridded historical climate, and downscaled climate models are not only increasing in spatial and temporal resolution but proliferating as well. Any rigorous assessment of uncertainty requires a computationally intensive sensitivity analysis accounting for various sources of uncertainty. The scientists fitting these models generally do not have the background in computer science required to take advantage of recent advances in web-service based data acquisition, remote high-powered data processing, or scientific workflow systems. Ecologists in the field of modeling are in need of a tractable platform that abstracts the inherent computational complexity required to incorporate the burgeoning field of coupled climate and ecological response modeling. In this paper we describe the computational challenges in species distribution modeling and solutions using scientific workflow systems. We focus on the Software for Assisted Species Modeling (SAHM) a package within VisTrails, an open-source scientific workflow system.

Bulletin of the Technical Committee on Data Engine↗

USGS2015

U.S. Geological Survey Unmanned Aircraft Systems (UAS) Roadmap 2014

The U.S. Department of the Interior (DOI) is responsible for protecting the natural resources and heritage contained on almost 20 percent of the land in the United States. This responsibility requires acquisition of remotely sensed data throughout vast lands, including areas that are remote and potentially dangerous to access. One promising new technology for data collection is unmanned aircraft systems (UAS), which may be better suited (achieving superior science, safety, and savings) than traditional methods. UAS, regardless of their size, have the same operational components: aircraft, payloads, communications unit, and operator control unit. The aircraft is the platform that flies and carries any required payloads. For Department of the Interior missions these payloads will be either a sensor or set of sensors that can acquire the specific type of remotely sensed data that is needed. The aircraft will also carry the payload that is responsible for transmitting live airborne video images, compass headings, and location information to the operator control unit. The communications unit, which transfers information between the aircraft and the operator control unit, consists of the hardware and software required to establish both uplink and downlink communications. Finally, the operator control unit both controls and monitors the aircraft and can be operated either by a pilot on the ground or autonomously. This Roadmap provides operational procedures and lessons learned from completed proof-of-concept UAS missions in areas such as wildlife management, resource monitoring, and public land inspections. This information provides not only an implementation framework but can also help increase the awareness by resource managers, scientists, and others of the ability of UAS technology to advance data quality, improve personnel safety, and reduce data acquisition costs.

Open-File Report↗

USGS2005

Geospatial decision support systems for societal decision making

While science provides reliable information to describe and understand the earth and its natural processes, it can contribute more. There are many important societal issues in which scientific information can play a critical role. Science can add greatly to policy and management decisions to minimize loss of life and property from natural and man-made disasters, to manage water, biological, energy, and mineral resources, and in general, to enhance and protect our quality of life. However, the link between science and decision-making is often complicated and imperfect. Technical language and methods surround scientific research and the dissemination of its results. Scientific investigations often are conducted under different conditions, with different spatial boundaries, and in different timeframes than those needed to support specific policy and societal decisions. Uncertainty is not uniformly reported in scientific investigations. If society does not know that data exist, what the data mean, where to use the data, or how to include uncertainty when a decision has to be made, then science gets left out -or misused- in a decision making process. This paper is about using Geospatial Decision Support Systems (GDSS) for quantitative policy analysis. Integrated natural -social science methods and tools in a Geographic Information System that respond to decision-making needs can be used to close the gap between science and society. The GDSS has been developed so that nonscientists can pose "what if" scenarios to evaluate hypothetical outcomes of policy and management choices. In this approach decision makers can evaluate the financial and geographic distribution of potential policy options and their societal implications. Actions, based on scientific information, can be taken to mitigate hazards, protect our air and water quality, preserve the planet's biodiversity, promote balanced land use planning, and judiciously exploit natural resources. Applications using the GDSS have demonstrated the benefits of utilizing science for policy decisions. Investment in science reduces decision-making uncertainty and reducing that uncertainty has economic value.

Boletin Geologico y Minero↗

USGS2025

Launching into societal benefits from the Surface Water and Ocean Topography (SWOT) mission

The 10th Surface Water and Ocean Topography (SWOT) Applications Meeting, held one year after the satellite's launch, highlighted significant milestones in mission progress and showcased the innovative work of SWOT Early Adopters (EA) using mission data products. Over 100 participants from diverse sectors convened to discuss operational applications leveraging SWOT's unprecedented water surface measurements. The meeting emphasized applied science efforts to enhance hydrology and oceanographic models. This summary highlights the breadth of operational and private-sector uses of SWOT data, emphasizing its potential to drive new innovations and deliver societal benefits, such as improved water resource management, flood prediction, and climate resilience.

Water Resources Research↗

USGS1998

Explore the virtual side of earth science

Scientists have always struggled to find an appropriate technology that could represent three-dimensional (3-D) data, facilitate dynamic analysis, and encourage on-the-fly interactivity. In the recent past, scientific visualization has increased the scientist's ability to visualize information, but it has not provided the interactive environment necessary for rapidly changing the model or for viewing the model in ways not predetermined by the visualization specialist. Virtual Reality Modeling Language (VRML 2.0) is a new environment for visualizing 3-D information spaces and is accessible through the Internet with current browser technologies. Researchers from the U.S. Geological Survey (USGS) are using VRML as a scientific visualization tool to help convey complex scientific concepts to various audiences. Kevin W. Laurent, computer scientist, and Maura J. Hogan, technical information specialist, have created a collection of VRML models available through the Internet at Virtual Earth Science (virtual.er.usgs.gov).

Fact Sheet↗

USGS2016

Selection of the InSight landing site

The selection of the Discovery Program InSight landing site took over four years from initial identification of possible areas that met engineering constraints, to downselection via targeted data from orbiters (especially Mars Reconnaissance Orbiter (MRO) Context Camera (CTX) and High-Resolution Imaging Science Experiment (HiRISE) images), to selection and certification via sophisticated entry, descent and landing (EDL) simulations. Constraints on elevation ( ≤ − 2.5   km "> ≤ − 2.5 km ≤−2.5 km for sufficient atmosphere to slow the lander), latitude (initially 15°S–5°N and later 3°N–5°N for solar power and thermal management of the spacecraft), ellipse size (130 km by 27 km from ballistic entry and descent), and a load bearing surface without thick deposits of dust, severely limited acceptable areas to western Elysium Planitia. Within this area, 16 prospective ellipses were identified, which lie ∼600 km north of the Mars Science Laboratory (MSL) rover. Mapping of terrains in rapidly acquired CTX images identified especially benign smooth terrain and led to the downselection to four northern ellipses. Acquisition of nearly continuous HiRISE, additional Thermal Emission Imaging System (THEMIS), and High Resolution Stereo Camera (HRSC) images, along with radar data confirmed that ellipse E9 met all landing site constraints: with slopes <15° at 84 m and 2 m length scales for radar tracking and touchdown stability, low rock abundance (<10 %) to avoid impact and spacecraft tip over, instrument deployment constraints, which included identical slope and rock abundance constraints, a radar reflective and load bearing surface, and a fragmented regolith ∼5 m thick for full penetration of the heat flow probe. Unlike other Mars landers, science objectives did not directly influence landing site selection.

Space Science Reviews↗

USGS2020

Preparing for geophysical science on the surface of the moon enabled by Artemis

Geophysical methods have been extremely successful in identifying resources on Earth as they provide a means of characterizing and mapping the sub-surface using data gathered on and above the target structures. Geophysics on the Moon will be an important tool for identifying key targets for geological prospecting, scientific sampling, assessing hazards and risks to crew and infrastructure, and determining the near subsurface and deeper workings of the lunar interior. Artemis will require 21st century geophysics instruments to advance lunar science and exploration, similar to how Apollo gathered geophysical data that is still leading to scientific discoveries 50 years later.

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