The Grand Canyon geologic field photograph collection contains 1,211 geotagged photographs collected during 43 years of geologic mapping from 1967 to 2010. The photographs document some key geologic features, structures, and rock unit relations that were used to compile nine geologic maps of the Grand Canyon region published at 1:100,000 scale, and many more maps published at 1:24,000 scale. Metadata for each photograph include description, date captured, coordinates, and a keyword system that places each photograph in one or more of the following categories: arches and windows, breccia pipes and collapse structures, faults and folds, igneous rocks, landslides and rockfalls, metamorphic rocks, sedimentary rocks, sinkholes, and springs and waterfalls. Original photograph slides are available at the Northern Arizona University Cline Library Special Collections and Archives.
The Geologic Field Photograph Map of the Grand Canyon Region, 1967–2010, is an interactive online map application that shows clusters of photograph thumbnails and popup windows that scale as users pan, zoom, and click around the map. The photographs can be filtered by category, searched based on date range, description, and keywords, and (or) downloaded. All information populated within the map is served from a ScienceBase record of the Grand Canyon field photograph collection that can be accessed at https://doi.org/10.5066/F7WS8SHW.
Director, Geology, Minerals, Energy, and Geophysics (GMEG) Science Center
U.S. Geological Survey
2255 North Gemini Drive
Flagstaff AZ 86001–1637
The U.S. Geological Survey collaborated with the U.S. Army Corps of Engineers, Louisville District, on a synoptic study of water quality at Nolin River Lake during August 2016. The purpose of the study was to develop a better understanding of the potential for interaction between groundwater and surface water at Nolin River Lake, Kentucky. Groundwater can have properties that are measurably different from those in adjacent surface water, and inflows and outflows can be an important component of water quality and quantity. An improved understanding of potential interaction of groundwater and surface water at Nolin River Lake may be used to refine lake-management strategies. This study (1) compiled and interpreted existing information to characterize the hydrogeological setting and implications for potential interaction of groundwater and surface water in the Nolin River Lake watershed; (2) collected transects of onsite water-quality parameters using an autonomous underwater vehicle (AUV) in areas with potential for interaction of groundwater and surface water, including five sites on Nolin River Lake and one site on the Nolin River; and (3) collected discrete water-quality and phytoplankton community data at the same six sites.
A review of existing hydrogeologic information did not indicate the presence of karst features adjacent to or beneath Nolin River Lake that would facilitate groundwater interaction with the reservoir. Observations leading to this conclusion include (1) limestone that is adjacent to the shoreline and perhaps beneath the lake, is overlain with siliciclastic rocks and fine-grained sediment that inhibits infiltration and development of karst features that encourage rapid groundwater flow; (2) the geologic deposits surrounding the reservoir are described as having limited or no potential for development of karst features, some exceptions may exist in tributary valleys; (3) very few karst features were mapped within 1 mile of the reservoir or in the area currently occupied by the reservoir; and (4) faults that intersect the reservoir but may not possess hydraulic properties that cause the faults to be conduits for groundwater flow. Groundwater interaction with reservoir tributaries is likely more common in areas of the watershed upstream from Nolin River Lake where karst hydrogeology is prevalent.
Results of water-quality surveys using an AUV from August 15 to 19, 2016, did not identify areas of anomalous values that might indicate groundwater inflows through preferential flow zones. Spatial distributions of water-quality parameters were generally uniform within each constant-depth layer. The constant-depth layers were selected to be above, within, and below the thermocline and ranged from the water surface to 25 feet. Surveys near the bottom of the reservoir that might have been more sensitive to groundwater inflows were not done because presurvey data were not available to indicate locations of obstacles that could ensnare the AUV. Water-quality data collected with the AUV did identify water-quality anomalies where stream tributaries were discharging to the reservoir.
The discrete water-quality samples indicated uniformity among the five reservoir sites. The riverine site that is immediately upstream from Nolin River Lake, however, had some unique water-quality characteristics relative to sites on the reservoir. The highest concentrations of nitrate plus nitrite as nitrogen (0.145 milligrams per liter [mg/L]), total phosphorous (0.07 mg/L), chlorophyll a (36.1 micrograms per liter), and pheophytin a (10.2 micrograms per liter) were measured at the Nolin River Lake riverine site (site 2NRR20034). The concentrations of nutrients and chlorophyll a at the riverine site did exceed the 25th percentile of median concentrations measured by the U.S. Environmental Protection Agency (EPA) at other lakes and reservoirs in EPA level IV ecoregion 71a. Concentrations of most nutrients and chlorophyll a at the five reservoir sites also exceeded the 25th percentile of median concentrations in EPA level IV ecoregion 72h. The exception was the concentrations of total phosphorus as phosphorus at the reservoir sites that were at or below the 25th percentile of median concentrations measured by EPA (0.03 mg/L). Concentrations of orthophosphate as phosphorus were less than the method detection limit of 0.004 mg/L at all sites. The phytoplankton community in Nolin River Lake was almost exclusively (greater than 90 percent of total phytoplankton abundance) cyanobacteria, also known as blue-green algae. A species of Cylindrospermopsis dominated the cyanobacterial community at the five reservoir sites, while Chroococcus microscopicus was most abundant at the riverine site. Cyanobacterial cell densities ranged from 10,000 to 198,067,460 cells per liter in five areas in the reservoir and from 4,800 to 73,751,253 cells per liter at the riverine site.
Multiple potential sources of water to Nolin River Lake include direct precipitation, overland flow, interflow, groundwater, and surface water. Understanding the exact contribution of each of these components to the water budget at Nolin River Lake may help the U.S. Army Corps of Engineers manage the water quality, water quantity, and biological communities in the reservoir. Additional hydrogeologic and water-quality data that builds on the results of this study may refine the inferences of this study; for example, deeper AUV surveys that target the largest fault zones might further the understanding of the potential for groundwater flow through those features. A complete understanding of the reservoir hydrology, however, may require the use of scientific methods intended for water bodies as large as Nolin River Lake, such as aerial infrared photography and imagery; water mass, chemical, and isotopic balance studies; geophysical measurements; and numerical simulations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195075","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers, Louisville District","usgsCitation":"Crain, A.S., Boldt, J.A., Bayless, E.R., Bunch, A.R., Young, J.L., Thomason, J.C., and Wolf, Z.L., 2019, Potential interaction of groundwater and surface water including autonomous underwater vehicle reconnaissance at Nolin River Lake, Kentucky, 2016: U.S. Geological Survey Scientific Investigations Report 2019–5075, 36 p., https://doi.org/10.3133/sir20195075.\n","productDescription":"Report: vi, 36 p.; Data Release","numberOfPages":"46","onlineOnly":"Y","ipdsId":"IP-085091","costCenters":[{"id":346,"text":"Indiana Water Science Center","active":true,"usgs":true},{"id":354,"text":"Kentucky Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":367882,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5075/sir20195075.pdf","text":"Report","size":"16.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5075"},{"id":367881,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5075/coverthb.jpg"},{"id":367883,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F798857D","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water-Quality Datasets from Synoptic Surveys in Nolin River Lake, Kentucky, using an Autonomous Underwater Vehicle, Discrete Sampling, and Depth Profiles, August 2016"}],"country":"United States","state":"Kentucky","otherGeospatial":"Nolin River Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.28387451171875,\n 37.25929865437848\n ],\n [\n -86.0504150390625,\n 37.25929865437848\n ],\n [\n -86.0504150390625,\n 37.40780092202727\n ],\n [\n -86.28387451171875,\n 37.40780092202727\n ],\n [\n -86.28387451171875,\n 37.25929865437848\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
9818 Bluegrass Parkway
Louisville, KY 40299–1906
Species distributions are determined by the interaction of multiple biotic and abiotic factors, which produces complex spatial and temporal patterns of occurrence. As habitats and climate change due to anthropogenic activities, there is a need to develop species distribution models that can quantify these complex range dynamics. In this paper, we develop a dynamic occupancy model that uses a spatial generalized additive model to estimate non-linear spatial variation in occupancy not accounted for by environmental covariates. The model is flexible and can accommodate data from a range of sampling designs that provide information about both occupancy and detection probability. Output from the model can be used to create distribution maps and to estimate indices of temporal range dynamics. We demonstrate the utility of this approach by modeling long-term range dynamics of 10 eastern North American birds using data from the North American Breeding Bird Survey. We anticipate this framework will be particularly useful for modeling species’ distributions over large spatial scales and for quantifying range dynamics over long temporal scales.
","language":"English","publisher":"Nature","doi":"10.1038/s41598-019-48851-5","usgsCitation":"Rushing, C.S., Royle, J.A., Ziolkowski, D., and Pardieck, K.L., 2019, Modeling spatially and temporally complex range dynamics when detection is imperfect: Scientific Reports, v. 9, 12805, 9 p., https://doi.org/10.1038/s41598-019-48851-5.","productDescription":"12805, 9 p.","ipdsId":"IP-098777","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":459911,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-019-48851-5","text":"Publisher Index Page"},{"id":367307,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2019-09-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Rushing, Clark S. 0000-0002-9283-6563","orcid":"https://orcid.org/0000-0002-9283-6563","contributorId":218851,"corporation":false,"usgs":true,"family":"Rushing","given":"Clark","email":"","middleInitial":"S.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":true,"id":770529,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":139626,"corporation":false,"usgs":true,"family":"Royle","given":"J.","email":"aroyle@usgs.gov","middleInitial":"Andrew","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":770530,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ziolkowski, David 0000-0002-2500-4417 dziolkowski@usgs.gov","orcid":"https://orcid.org/0000-0002-2500-4417","contributorId":195409,"corporation":false,"usgs":true,"family":"Ziolkowski","given":"David","email":"dziolkowski@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":770531,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pardieck, Keith L. 0000-0003-2779-4392 kpardieck@usgs.gov","orcid":"https://orcid.org/0000-0003-2779-4392","contributorId":4104,"corporation":false,"usgs":true,"family":"Pardieck","given":"Keith","email":"kpardieck@usgs.gov","middleInitial":"L.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":770532,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70204633,"text":"70204633 - 2019 - Climatic correlates of white pine blister rust infection in whitebark pine in the Greater Yellowstone Ecosystem","interactions":[],"lastModifiedDate":"2019-08-12T09:30:33","indexId":"70204633","displayToPublicDate":"2019-08-07T09:01:46","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1689,"text":"Forests","active":true,"publicationSubtype":{"id":10}},"title":"Climatic correlates of white pine blister rust infection in whitebark pine in the Greater Yellowstone Ecosystem","docAbstract":"Whitebark pine, a foundation species at tree line in the Western U.S. and Canada, has declined due to native mountain pine beetle epidemics, wildfire, and white pine blister rust. These declines are concerning for the multitude of ecosystem and human benefits provided by this species. Understanding climatic correlates associated with spread is needed to successfully manage impacts from forest pathogens. In the Greater Yellowstone Ecosystem since 2000 mountain pine beetles have killed 75 percent of the mature cone-bearing trees, and 40.9 percent of monitored trees have been infected with white pine blister rust. We identified models of white pine blister rust infection that indicate an August and September interaction between relative humidity and temperature were better predictors of white pine blister rust infection in whitebark pine than location and site characteristics in the Greater Yellowstone Ecosystem. The climate conditions conducive to white pine blister rust occur throughout the ecosystem, but larger trees in relatively warm and humid conditions were more likely to be infected between 2000 and 2018. We mapped the infection probability over the past two decades to identify coarse-scale patterns of climate conditions conducive to white pine blister rust infection in whitebark pine.","language":"English","publisher":"MDPI","doi":"10.3390/f10080666","usgsCitation":"Thoma, D., Shanahan, E.K., and Irvine, K., 2019, Climatic correlates of white pine blister rust infection in whitebark pine in the Greater Yellowstone Ecosystem: Forests, v. 10, no. 8, 16 p., https://doi.org/10.3390/f10080666.","productDescription":"16 p.","ipdsId":"IP-109650","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":467389,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/f10080666","text":"Publisher Index Page"},{"id":366366,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Yellowstone National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.32421875,\n 42.76314586689492\n ],\n [\n -108.2373046875,\n 42.76314586689492\n ],\n [\n -108.2373046875,\n 45.72152152227954\n ],\n [\n -112.32421875,\n 45.72152152227954\n ],\n [\n -112.32421875,\n 42.76314586689492\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","issue":"8","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-08-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Thoma, David","contributorId":190258,"corporation":false,"usgs":false,"family":"Thoma","given":"David","affiliations":[],"preferred":false,"id":767850,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shanahan, Erin K.","contributorId":217938,"corporation":false,"usgs":false,"family":"Shanahan","given":"Erin","email":"","middleInitial":"K.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":767851,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Irvine, Kathryn 0000-0002-6426-940X","orcid":"https://orcid.org/0000-0002-6426-940X","contributorId":217937,"corporation":false,"usgs":true,"family":"Irvine","given":"Kathryn","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":767849,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70205961,"text":"70205961 - 2019 - Evaluation of stream and wetlands restoration using UAS-based thermal infrared mapping","interactions":[],"lastModifiedDate":"2021-04-27T16:13:54.55326","indexId":"70205961","displayToPublicDate":"2019-07-29T06:51:18","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of stream and wetlands restoration using UAS-based thermal infrared mapping","docAbstract":"Large-scale wetland restoration often focuses on repairing the hydrologic connections degraded by anthropogenic modifications. Of these hydrologic connections, groundwater discharge is an important target, as these surface water ecosystem control points are important to thermal stability, among other ecosystem services. However, evaluating the effectiveness of the restoration activities on establishing groundwater discharge connection is often difficult over the vast area and often challenging or inaccessible terrain of wetlands. Unoccupied aerial systems (UAS) are now routinely used for collecting aerial imagery and creating digital surface models (DSM). Lightweight thermal infrared (TIR) sensors provide another payload option for generation of sub-meter resolution aerial TIR orthophotos. This technology allows for the rapid and safe survey of groundwater discharge areas. Aerial TIR water-surface data were collected March 2019 at Tidmarsh Farms, a former commercial cranberry peatland located in coastal Massachusetts, USA (41°54'17.6\"N 70°34'17.4\"W), where stream and wetland restoration actions were completed in 2016. Here we present a 0.4 km2 georeferenced, temperature calibrated TIR orthophoto of the area. The image represents a mosaic of nearly 900 TIR images captured by UAS in a single morning with a total flight time of 36 minutes, and is supported by a DSM derived from UAS visible imagery. The survey was conducted in winter to maximize temperature contrast between relatively warm groundwater and colder ambient surface environment; lower-density groundwater rises above cool surface waters and thus can be imaged by a UAS. The resulting TIR orthomosaic shows fine detail of seepage distribution and downstream influence along the several restored channel forms, which was an objective of the ecological restoration design. The restored stream channel has increased connectivity to peatland groundwater discharge, reducing the ecosystem thermal stressors. Such aerial techniques can be used to guide ecological restoration design and assess post-restoration outcomes, especially in settings where ecosystem structure and function is governed by groundwater and surface water interaction.","language":"English","publisher":"MDPI","doi":"10.3390/w11081568","usgsCitation":"Harvey, M., Hare, D., Hackman, A., Davenport, G., Haynes, A., Helton, A., Lane, J.W., and Briggs, M., 2019, Evaluation of stream and wetlands restoration using UAS-based thermal infrared mapping: Water, v. 11, no. 8, 1568, 13 p., https://doi.org/10.3390/w11081568.","productDescription":"1568, 13 p.","ipdsId":"IP-109877","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":467416,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w11081568","text":"Publisher Index Page"},{"id":368290,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"8","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-07-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Harvey, Mark","contributorId":190941,"corporation":false,"usgs":false,"family":"Harvey","given":"Mark","email":"","affiliations":[],"preferred":false,"id":773064,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hare, Danielle K.","contributorId":219738,"corporation":false,"usgs":false,"family":"Hare","given":"Danielle","middleInitial":"K.","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":773065,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hackman, Alex","contributorId":219739,"corporation":false,"usgs":false,"family":"Hackman","given":"Alex","email":"","affiliations":[{"id":40057,"text":"Massachusetts DER","active":true,"usgs":false}],"preferred":false,"id":773066,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Davenport, Glorianna","contributorId":219740,"corporation":false,"usgs":false,"family":"Davenport","given":"Glorianna","email":"","affiliations":[{"id":40058,"text":"The Living Observatory","active":true,"usgs":false}],"preferred":false,"id":773067,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Haynes, Adam","contributorId":216657,"corporation":false,"usgs":false,"family":"Haynes","given":"Adam","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":773068,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Helton, Ashley","contributorId":219741,"corporation":false,"usgs":false,"family":"Helton","given":"Ashley","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":773069,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lane, John W. 0000-0002-3558-243X","orcid":"https://orcid.org/0000-0002-3558-243X","contributorId":219742,"corporation":false,"usgs":true,"family":"Lane","given":"John","email":"","middleInitial":"W.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":773070,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Briggs, Martin 0000-0003-3206-4132","orcid":"https://orcid.org/0000-0003-3206-4132","contributorId":219737,"corporation":false,"usgs":true,"family":"Briggs","given":"Martin","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":773063,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70203561,"text":"70203561 - 2019 - Mapping cropland extent of Southeast and Northeast Asia using multi-year time-series Landsat 30-m data using Random Forest classifier on Google Earth Engine","interactions":[],"lastModifiedDate":"2019-05-22T16:12:58","indexId":"70203561","displayToPublicDate":"2019-05-22T16:11:44","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2027,"text":"International Journal of Applied Earth Observation and Geoinformation","active":true,"publicationSubtype":{"id":10}},"title":"Mapping cropland extent of Southeast and Northeast Asia using multi-year time-series Landsat 30-m data using Random Forest classifier on Google Earth Engine","docAbstract":"Cropland extent maps are useful components for assessing food security. Ideally, such products are a useful addition to countrywide agricultural statistics since they are not politically biased and can be used to calculate cropland area for any spatial unit from an individual farm to various administrative unites (e.g., state, county, district) within and across nations, which in turn can be used to estimate agricultural productivity as well as degree of disturbance on food security from natural disasters and political conflict. However, existing cropland extent maps over large areas (e.g., Country, region, continent, world) are derived from coarse resolution imagery (250 m to 1 km pixels) and have many limitations such as missing fragmented and\\or small farms with mixed signatures from different crop types and\\or farming practices that can be, confused with other land cover. As a result, the coarse resolution maps have limited useflness in areas where fields are small (<1 ha), such as in Southeast Asia. Furthermore, coarse resolution cropland maps have known uncertainties in both geo-precision of cropland location as well as accuracies of the product. To overcome these limitations, this research was conducted using multi-date, multi-year 30-m Landsat time-series data for 3 years chosen from 2013 to 2016 for all Southeast and Northeast Asian Countries (SNACs), which included 7 refined agro-ecological zones (RAEZ) and 12 countries (Indonesia, Thailand, Myanmar, Vietnam, Malaysia, Philippines, Cambodia, Japan, North Korea, Laos, South Korea, and Brunei). The 30-m (1 pixel = 0.09 ha) data from Landsat 8 Operational Land Imager (OLI) and Landsat 7 Enhanced Thematic Mapper (ETM+) were used in the study. Ten Landsat bands were used in the analysis (blue, green, red, NIR, SWIR1, SWIR2, Thermal, NDVI, NDWI, LSWI) along with additional layers of standard deviation of these 10 bands across 1 year, and global digital elevation model (GDEM)-derived slope and elevation bands. To reduce the impact of clouds, the Landsat imagery was time-composited over four time-periods (Period 1: January- April, Period 2: May-August, and Period 3: September-December) over 3-years. Period 4 was the standard deviation of all 10 bands taken over all images acquired during the 2015 calendar year. These four period composites, totaling 42 band data-cube, were generated for each of the 7 RAEZs. The reference training data (N = 7849) generated for the 7 RAEZ using sub-meter to 5-m very high spatial resolution imagery (VHRI) helped generate the knowledge-base to separate croplands from non-croplands. This knowledge-base was used to code and run a pixel-based random forest (RF) supervised machine learning algorithm on the Google Earth Engine (GEE) cloud computing environment to separate croplands from non-croplands. The resulting cropland extent products were evaluated using an independent reference validation dataset (N = 1750) in each of the 7 RAEZs as well as for the entire SNAC area. For the entire SNAC area, the overall accuracy was 88.1% with a producer’s accuracy of 81.6% (errors of omissions = 18.4%) and user’s accuracy of 76.7% (errors of commissions = 23.3%). For each of the 7 RAEZs overall accuracies varied from 83.2 to 96.4%. Cropland areas calculated for the 12 countries were compared with country areas reported by the United Nations Food and Agriculture Organization and other national cropland statistics resulting in an R2 value of 0.93. The cropland areas of provinces were compared with the province statistics that showed an R2 = 0.95 for South Korea and R2 = 0.94 for Thailand. The cropland products are made available on an interactive viewer at www.croplands.org and for download at National Aeronautics and Space Administration’s (NASA) Land Processes Distributed Active Archive Center (LP DAAC): https://lpdaac.usgs.gov/node/1281.
Flood-inundation maps for a 14.5-mile reach of the Amite River and a 20.2-mile reach of the Comite River from State Highway 64 to U.S. Highway 190 were created by the U.S. Geological Survey (USGS) in cooperation with the City of Central, Louisiana. These maps, which can be accessed through an interactive mapper at the USGS Flood Inundation Mapping Program website and from a companion USGS data release, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgages Amite River at Magnolia, La., (07377300) and Comite River near Comite, La. (07378000).
Flood profiles were computed for the Amite and Comite River reaches by using the two-dimensional (2D), finite-volume numerical modeling options in the U.S. Army Corps of Engineers Hydrologic Engineering Center’s River Analysis System (USACE HEC-RAS) software version 5.0.3. Models were calibrated to the current (2018) stage-discharge relations at the Amite River at Magnolia, La., and Comite River near Comite, La., streamgages, water-surface profiles from the March and August 2016 floods, and documented high-water marks from the flood of August 2016.
The hydraulic models were used to compute 37 individual water-surface profiles (21 for the Amite River and 16 for the Comite River) at 1.0-foot intervals ranging from the National Weather Service flood stage to the highest peak on record at the two streamgages. The 37 simulated water-surface profiles were used with a light detection and ranging-derived digital elevation model to delineate the flood extent and associated depth at each water level. The delineated areas (inundation maps) were merged into 127 combinations or possible flooding scenarios based on annual peak stage information from the two streamgaging stations.
The availability of these maps, along with real-time data delivered via the internet, will provide emergency management personnel and residents with information that is critical for flood-response activities such as evacuations and road closures, as well as for recovery efforts after floods.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195028","collaboration":"Prepared in cooperation with the City of Central, Louisiana","usgsCitation":"Storm, J.B., 2019, Flood-inundation maps for the Amite and Comite Rivers from State Highway 64 to U.S. Highway 190 at Central, Louisiana: U.S. Geological Survey Scientific Investigations Report 2019–5028, 20 p., https://doi.org/10.3133/sir20195028.\n","productDescription":"Report: viii, 20 p.; Data Release; Flood Inundation Mapper","numberOfPages":"32","onlineOnly":"Y","ipdsId":"IP-100404","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":363430,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5028/sir20195028.pdf","text":"Report","size":"6.91 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5028"},{"id":363431,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PQKSYF","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Flood Inundation Maps for the Amite and Comite Rivers from State Highway 64 to U.S. Highway 190 – City of Central, Louisiana"},{"id":363429,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5028/coverthb.jpg"},{"id":363432,"rank":4,"type":{"id":4,"text":"Application Site"},"url":"https://www.usgs.gov/mission-areas/water-resources/science/flood-inundation-mapping-fim-program?qt-science_center_objects=0#qt-science_center_objects","text":"Flood Inundation Mapper","description":"Flood Inundation Mapper"}],"country":"United States","state":"Louisiana ","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.3123779296875,\n 28.859107573773\n ],\n [\n -93.3123779296875,\n 31.475524020001806\n ],\n [\n -89.879150390625,\n 31.475524020001806\n ],\n [\n -89.879150390625,\n 28.859107573773\n ],\n [\n -93.3123779296875,\n 28.859107573773\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Lower Mississippi Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Ste 100
Nashville, TN 37211
A binational study was initiated to update statistical equations that are used to estimate the magnitude and frequency of peak flows on streams in Manitoba and Ontario, Canada, and Minnesota that are contained within the binational Lake of the Woods–Rainy River Basin upstream from Kenora, Ontario, Canada. Hydraulic engineers use peak streamflow data to inform designs of bridges, culverts, and dams, and water managers use peak streamflow data to inform regulation and planning activities. However, long-term streamflow measurements are available at few locations along the more than 20,000 miles of stream/ditch networks within the binational Lake of the Woods–Rainy River Basin upstream from Kenora, Ontario, Canada.
Estimates of peak-flow magnitudes for 66.7-, 50-, 20-, 10-, 4-, 2-, 1-, and 0.2-percent annual exceedance probabilities equivalent to annual flood-frequency recurrence intervals of 1.5-, 2-, 5-, 10-, 25-, 50-, 100-, and 500-year recurrence intervals, respectively, are presented for 49 streamgages in Minnesota and adjacent areas in the Province of Ontario, Canada, based on data collected through water year 2013. Peak-flow frequency information was subsequently used in regression analyses to develop equations relating peak flows for selected recurrence intervals to various basin and climatic characteristics.
The study area includes 49 streamgages located in the binational Lake of the Woods–Rainy River Basin upstream from Kenora, Ontario, Canada, and is represented by southern portions of the Canadian Provinces of Manitoba (2 percent) and Ontario (56 percent) and the northern portion of the U.S. State of Minnesota (42 percent). The study area was represented by three regions that were defined in previous studies in the U.S. State of Minnesota and another in the Canadian Province of Ontario. The two Minnesota regions A and B were developed using a multiple regression method and hydrologic landscape units were used to validate regions in Minnesota. The Ontario region A was developed using a multiple regression method and standardized residuals from the 100-year recurrence intervals.
Canadian maximum instantaneous peak-flow data were converted from a calendar year to a water year (October 1 to September 30) and where the annual maximum instantaneous peak-flow value was not available in HYDAT, the Sangal method was applied to known average daily flow values to estimate an annual maximum instantaneous peak-flow value. Geographic information system software was used to calculate eight characteristics investigated as potential explanatory variables in the regression analyses.
The procedure for estimating peak-flow frequency for selected exceedance probabilities for a specific ungaged site depends on whether the site is near a streamgage on the same stream or is on an ungaged stream. For an ungaged site near a streamgage on the same stream, the drainage-area ratio method can be used. For an ungaged site on an ungaged stream, the regional regression equations developed for this study should be used.
All equations presented in this study will be incorporated into StreamStats, a web-based geographic information system tool developed by the U.S. Geological Survey. StreamStats allows users to obtain streamflow statistics, basin characteristics, and other information for user-selected locations on streams through an interactive map.
Director, Upper Midwest Water Science Center
U.S. Geological Survey
2280 Woodale Drive
Mounds View, MN 55112
Development of a time series of spatially distributed climate data is an important step in the process of developing physically based environmental models requiring distributed inputs of climate data beyond what is available from observations collected at climate stations. To prepare inputs required for model-mapping units across the study area, climate data (temperature and precipitation) are distributed by combining data from gridded surfaces of mean-monthly climate-data values with (often) widely spaced daily point observations. Examples of climate-data files used to develop PRMS-formatted input files for the Merced River Basin Precipitation-Runoff Modeling System (PRMS) are included in this manual.
The Draper Climate-Distribution Software Suite (Draper Suite) consists of the Draper climate-distribution program (Draper) and several supporting pre- and post-processing applications. Draper combines spatially distributed input in the form of monthly averaged values for precipitation, maximum temperature, and minimum temperature with daily observed data from climate stations to estimate distributed climate-data values at predefined locations across a study area (typically a drainage basin) on a daily time step. Alternative methods are used when station data are limited or missing for a particular day. Draper uses a set of required and optional input and output files with defined formats and naming conventions. A shell application also is available to manage multiple runs of the Draper application.
Other applications in the Draper Suite include (1) a tool to find and interactively remove outliers in the input data, (2) a tool to check and enforce a minimum daily temperature range, and (3) a tool to view output diagnostic information as time-series graphs. These tools can be used iteratively to evaluate and improve the results from Draper as part of a workflow involving physically based environmental models, such as the Precipitation-Runoff Modeling System (PRMS).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm7C22","collaboration":"Prepared in cooperation with the California Department of Water Resources","usgsCitation":"Donovan, J.M., and Koczot, K.M., 2019, User’s manual for the Draper climate-distribution software suite with data‑evaluation tools: U.S. Geological Survey Techniques and Methods 7-C22, 55 p., https://doi.org/10.3133/tm7C22. ","productDescription":"viii, 55 p","numberOfPages":"68","onlineOnly":"Y","ipdsId":"IP-086388","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":362190,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/07/c22/coverthb.jpg"},{"id":362191,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/07/c22/tm7c22.pdf","text":"Report","size":"6.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 7-C22"},{"id":365983,"rank":3,"type":{"id":4,"text":"Application Site"},"url":"https://code.usgs.gov/cawsc/draper","text":"Source code and executables","linkHelpText":"- Users are required to create an account to access the distribution"}],"contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Context
Movement of prey on hydrologically pulsed, spatially heterogeneous wetlands can result in transient, high prey concentrations, when changes in landscape features such as connectivity between flooded areas alternately facilitate and impede prey movement. Predators track and exploit these concentrations, depleting them as they arise.
Objectives
We sought to describe how prey pulses of fish rapidly form and persist on wetland landscapes, while enduring constant consumption by wading birds, without being fully depleted. Specifically, we questioned how is the predator–prey relationship mediated by interactions between animal movement and dynamic landscape connectivity?
Methods
Two models were developed of the predator–prey-landscape system with qualitatively different representations of space, to identify and quantify prey pulsing dynamics that were robust across modeled assumptions. The first included a homogeneous landscape described by simple geometry, and implicit fish movement as wetland volume contracts. The second modeled transverse movement across a heterogeneous landscape, with isolated drying patches.
Results
Both models produced rapid fish prey concentrations as the wetland dried to shallow water depths. These conditions are critical for making prey available to wading birds. Fish were also rapidly depleted by birds, representing daily caloric intake supporting birds. Model 1 provided average estimates across the modeled domain. Model 2 mapped locations of emerging prey hotspots on the landscape through time.
Conclusions
Our models tracked predator, prey, and landscape dynamics in parallel, inducing systems dynamics from empirical observations. Explicit inclusion of dynamic wetland hydrologic connectivity, a key landscape mechanism, allowed for a comprehensive picture of links between landscape dynamics and the adapted predator–prey system.
Aim
In Southern California, native woody shrublands known as chaparral support exceptional biodiversity. However, large‐scale conversion of chaparral into largely exotic herbaceous cover is a major ecological threat and serious conservation concern. Due to substantial uncertainty regarding the causes and extent of this vegetation change, we aimed to quantify the primary drivers of and map potentially vulnerable locations for vegetation type conversion from woody into herbaceous cover.
Location
Santa Monica Mountains National Recreational Area, Southern California, USA.
Methods
We used air photograph image interpretation to quantify the extent to which chaparral shrublands transitioned to herbaceous cover from 1943 to 2014 across nearly 800 randomly located plots. Comparing plots that remained chaparral to those that converted to herbaceous cover, we performed hierarchical partitioning to quantify the independent contribution of a range of explanatory variables, and then used classification trees to explore variable interactions. We also developed a spatial model to create a seamless map delineating relative probability of type conversion.
Results
Of the original plots that were chaparral in 1943, 284 (36%) changed cover by 2014, with 79 completely converting, and 142 mostly converting to herbaceous cover. The primary mechanism behind shrubland decline and replacement was short intervals between fires (<=10 years), and type conversion was most likely to occur in arid parts of the landscape with low topographic heterogeneity and close proximity to trails and roads. Predictive maps delineated several hotspots with environmental conditions similar to those of type‐converted plots.
Main conclusions
Chaparral type conversion is a widespread conservation concern, and results here suggest that short‐interval fire and landscape disturbance are the most likely factors to exacerbate it, particularly in water‐limited portions of the landscape where chaparral is subject to greater physiological stress and slower recovery. Reducing fire ignitions and mapping vulnerable areas may be important strategies for prevention.
No abstract available.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Free and Open Source Software for Geospatial (FOSS4G)","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Free and Open Source Software for Geospatial (FOSS4G)","conferenceDate":"May 14-16, 2018","conferenceLocation":"St Louis, MO","language":"English","publisher":"OSGeo","usgsCitation":"Baumer, W., Powell, L., and Varanka, D.E., 2018, Mapping interactive geospatial linked data, in Free and Open Source Software for Geospatial (FOSS4G), St Louis, MO, May 14-16, 2018, 4 p.","productDescription":"4 p.","ipdsId":"IP-095212","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":369107,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Baumer, William 0000-0001-8011-841X","orcid":"https://orcid.org/0000-0001-8011-841X","contributorId":204666,"corporation":false,"usgs":true,"family":"Baumer","given":"William","email":"","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":734627,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Powell, Logan J. 0000-0002-0528-3092 ljpowell@usgs.gov","orcid":"https://orcid.org/0000-0002-0528-3092","contributorId":201294,"corporation":false,"usgs":true,"family":"Powell","given":"Logan J.","email":"ljpowell@usgs.gov","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":false,"id":734626,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Varanka, Dalia E. 0000-0003-2857-9600 dvaranka@usgs.gov","orcid":"https://orcid.org/0000-0003-2857-9600","contributorId":1296,"corporation":false,"usgs":true,"family":"Varanka","given":"Dalia","email":"dvaranka@usgs.gov","middleInitial":"E.","affiliations":[{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true},{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":734625,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70199787,"text":"70199787 - 2018 - Crop water productivity estimation with hyperspectral remote sensing","interactions":[],"lastModifiedDate":"2020-05-27T15:58:19.713875","indexId":"70199787","displayToPublicDate":"2018-12-11T10:48:12","publicationYear":"2018","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"5","title":"Crop water productivity estimation with hyperspectral remote sensing","docAbstract":"Crop water productivity (CWP) is the ratio of accumulated crop biomass or yield (Y) to the water utilized to produce it, which is typically estimated using transpiration (ETC). CWP is an important metric to test and monitor water-saving strategies in agroecosystems across the globe. Red and near-infrared broadbands have been used to estimate CWP, because they capture biophysical constraints based on crop-light interaction principles at pixel level (e.g., 30-meter resolution) over large areas through time. Hyperspectral remote sensing, which allows for the more precise measurement of crop-light interactions at higher spectral resolution, should in theory provide higher accuracy in CWP estimation but has been underutilized by the remote sensing community due to computational challenges and lack of availability. In this study, a simple methodology is presented to demonstrate how CWP could be estimated using hyperspectral remote sensing. Due to a lack of hyperspectral data, Landsat-7 Enhanced Thematic Mapper Plus (ETM+) data were used for the demonstration. Landsat is a broadband sensor that provides considerable spectral information for CWP estimation. New bands were identified in the workflow outside the typical Landsat bands used to estimate CWP and its components (Y and ETC). Landsat bands 1 and 3 were the most effective at estimating CWP and Y with an R2 of 0.72 (RMSE = 0.50 kg m−3) and 0.64 (RMSE = 0.31 kg m−2), respectively. All of the bands were poor at estimating ETC, with Landsat bands 1 and 7 being the most highly correlated (R2 = 0.13, RMSE = 0.08 m). Future work should train models with multiple estimates of CWP and Y over the growing season, while ETC may be better estimated with thermal infrared bands not considered in this study. Finally, studies should also consider estimating CWP categorically, instead of continuously, if the same objectives of testing and monitoring are met.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Hyperspectral remote sensing of vegetation: Advanced applications in remote Sensing of agricultural crops and natural vegetation","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Taylor & Francis","usgsCitation":"Marshall, M., Aneece, I.P., Foley, D., Xueliang, C., and Biggs, T., 2018, Crop water productivity estimation with hyperspectral remote sensing, chap. 5 of Hyperspectral remote sensing of vegetation: Advanced applications in remote Sensing of agricultural crops and natural vegetation, v. 4, p. 79-96.","productDescription":"18 p.","startPage":"79","endPage":"96","ipdsId":"IP-097174","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":375087,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":375086,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.taylorfrancis.com/books/9780429431166/chapters/10.1201/9780429431166-5"}],"volume":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Marshall, Michael","contributorId":145855,"corporation":false,"usgs":false,"family":"Marshall","given":"Michael","affiliations":[{"id":16265,"text":"Dept. of Geography, UC Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":746604,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Aneece, Itiya P. 0000-0002-1201-5459","orcid":"https://orcid.org/0000-0002-1201-5459","contributorId":208265,"corporation":false,"usgs":true,"family":"Aneece","given":"Itiya","middleInitial":"P.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":746603,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Foley, Daniel 0000-0002-2051-6325","orcid":"https://orcid.org/0000-0002-2051-6325","contributorId":208266,"corporation":false,"usgs":true,"family":"Foley","given":"Daniel","email":"","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":746605,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Xueliang, Cai","contributorId":208267,"corporation":false,"usgs":false,"family":"Xueliang","given":"Cai","email":"","affiliations":[],"preferred":false,"id":746606,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Biggs, Trent","contributorId":208268,"corporation":false,"usgs":false,"family":"Biggs","given":"Trent","affiliations":[],"preferred":false,"id":746607,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70201006,"text":"ofr20181185 - 2018 - Interactive tool to estimate groundwater elevations in central and eastern North Dakota","interactions":[],"lastModifiedDate":"2018-12-05T14:44:37","indexId":"ofr20181185","displayToPublicDate":"2018-12-04T15:39:45","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-1185","displayTitle":"Interactive Tool to Estimate Groundwater Elevations in Central and Eastern North Dakota","title":"Interactive tool to estimate groundwater elevations in central and eastern North Dakota","docAbstract":"This report describes an interactive tool (NDakGWtool) in which a statistical model is developed using locally weighted regression to estimate monthly mean groundwater elevations for a specified latitude and longitude, referred to as the “user-specified location.” For each user-specified location, seven models are developed for each month from April through October. Localized, high spatial-resolution maps of estimated monthly mean groundwater surface elevations are produced from the models. The tool was evaluated for glacial drift aquifers of the 32-county study area in central and eastern North Dakota. Although groundwater elevations from 1960 to 2017 were available to develop the tool, groundwater elevations from 1995 to 2015 were used for model testing and development of the model domain. There are 413 grid cells of 0.1-degree latitude by 0.1-degree longitude size in the model domain, and the tool produces maps of estimated monthly mean groundwater surface elevations for the cell containing the user-specified location. Additionally, the NDakGWtool produces maps of estimated groundwater depth below land surface and ArcGIS files of estimated groundwater surface elevations and groundwater depth below land surface. The tool is composed of four main components: data input, statistical model, output, and user-interactive process.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181185","collaboration":"Prepared in cooperation with Natural Resources Conservation Service","usgsCitation":"Nustad, R.A., Damschen, W.C., and Vecchia, A.V., 2018, Interactive tool to estimate groundwater elevations in central and eastern North Dakota: U.S. Geological Survey Open-File Report 2018–1185, 24 p., https://doi.org/10.3133/ofr20181185.","productDescription":"Report: vi, 24; Appendix","numberOfPages":"34","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-090716","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":359877,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1185/coverthb.jpg"},{"id":359878,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1185/ofr20181185.pdf","text":"Report","size":"6.86 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018–1185"},{"id":359880,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2018/1185/ofr20181185_appendix.zip","text":"Appendix","size":"27.6 MB","linkFileType":{"id":6,"text":"zip"},"description":"OFR 2018–1185 Appendix","linkHelpText":"R Documentation"}],"country":"United States","state":"North Dakota","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503
We integrated field measurements, hydroponic experiments, microscopy, and spectroscopy to investigate the effect of Ca(II) on dissolved U(VI) uptake by plants in 1 mM HCO3– solutions at circumneutral pH. The accumulation of U in plants (3.1–21.3 mg kg–1) from the stream bank of the Rio Paguate, Jackpile Mine, New Mexico served as a motivation for this study. Brassica junceawas the model plant used for the laboratory experiments conducted over a range of U (30–700 μg L–1) and Ca (0–240 mg L–1) concentrations. The initial U uptake followed pseudo-second-order kinetics. The initial U uptake rate (V0) ranged from 4.4 to 62 μg g–1 h–1 in experiments with no added Ca and from 0.73 to 2.07 μg g–1 h–1 in experiments with 12 mg L–1 Ca. No measurable U uptake over time was detected for experiments with 240 mg L–1 Ca. Ternary Ca–U–CO3complexes may affect the decrease in U bioavailability observed in this study. Elemental X-ray mapping using scanning transmission electron microscopy–energy-dispersive spectrometry detected U–P-bearing precipitates within root cell walls in water free of Ca. These results suggest that root interactions with Ca and carbonate in solution affect the bioavailability of U in plants. This study contributes relevant information to applications related to U transport and remediation of contaminated sites.
","language":"English","publisher":"ACS","doi":"10.1021/acs.est.8b02724","usgsCitation":"El Hayek, E., Torres, C., Rodriguez-Freire, L., Blake, J., De Vore, C.L., Brearley, A.J., Spilde, M.N., Cabaniss, S., Ali, A.S., and Cerrato, J.M., 2018, Effect of calcium on the bioavailability of dissolved uranium(VI) in plant roots under circumneutral pH: Environmental Science & Technology, v. 52, no. 22, p. 13089-13098, https://doi.org/10.1021/acs.est.8b02724.","productDescription":"10 p.","startPage":"13089","endPage":"13098","ipdsId":"IP-096227","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":460811,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/6341987","text":"External Repository"},{"id":360794,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"52","issue":"22","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2018-11-09","publicationStatus":"PW","contributors":{"authors":[{"text":"El Hayek, Eliane","contributorId":207797,"corporation":false,"usgs":false,"family":"El Hayek","given":"Eliane","email":"","affiliations":[{"id":36307,"text":"University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":755189,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Torres, Chris","contributorId":211908,"corporation":false,"usgs":false,"family":"Torres","given":"Chris","email":"","affiliations":[{"id":36307,"text":"University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":755190,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rodriguez-Freire, Lucia","contributorId":211909,"corporation":false,"usgs":false,"family":"Rodriguez-Freire","given":"Lucia","email":"","affiliations":[{"id":38351,"text":"New Jersey Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":755191,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Blake, Johanna M. 0000-0003-4667-0096","orcid":"https://orcid.org/0000-0003-4667-0096","contributorId":211907,"corporation":false,"usgs":true,"family":"Blake","given":"Johanna M.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":755188,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"De Vore, Cherie L.","contributorId":211910,"corporation":false,"usgs":false,"family":"De Vore","given":"Cherie","email":"","middleInitial":"L.","affiliations":[{"id":36307,"text":"University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":755192,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Brearley, Adrian J.","contributorId":211911,"corporation":false,"usgs":false,"family":"Brearley","given":"Adrian","email":"","middleInitial":"J.","affiliations":[{"id":36307,"text":"University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":755193,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Spilde, Michael N.","contributorId":211912,"corporation":false,"usgs":false,"family":"Spilde","given":"Michael","email":"","middleInitial":"N.","affiliations":[{"id":36307,"text":"University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":755194,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Cabaniss, Stephen","contributorId":211913,"corporation":false,"usgs":false,"family":"Cabaniss","given":"Stephen","email":"","affiliations":[{"id":36307,"text":"University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":755195,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Ali, Abdul-Mehdi S.","contributorId":211914,"corporation":false,"usgs":false,"family":"Ali","given":"Abdul-Mehdi","email":"","middleInitial":"S.","affiliations":[{"id":36307,"text":"University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":755196,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Cerrato, Jose M.","contributorId":211915,"corporation":false,"usgs":false,"family":"Cerrato","given":"Jose","email":"","middleInitial":"M.","affiliations":[{"id":36307,"text":"University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":755197,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70200030,"text":"70200030 - 2018 - Spatial decision‐support tools to guide restoration and seed‐sourcing in the Desert Southwest","interactions":[],"lastModifiedDate":"2018-10-11T11:29:36","indexId":"70200030","displayToPublicDate":"2018-10-11T11:29:18","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Spatial decision‐support tools to guide restoration and seed‐sourcing in the Desert Southwest","docAbstract":"Altered disturbance regimes and shifting climates have increased the need for large‐scale restoration treatments across the western United States. Seed‐sourcing remains a considerable challenge for revegetation efforts, particularly on public lands where policy favors the use of native, locally sourced plant material to avoid maladaptation. An important area of emphasis for public agencies has been the development of spatial tools to guide selection of genetically appropriate seed. When genetic information is not available, current seed transfer guidelines stipulate use of climate‐based or provisional seed transfer zones, which serve as a proxy for local adaptation by representing climate gradients to which plants are commonly adapted. Despite this guidance, little emphasis has been placed on identifying best practices for deriving provisional seed zones or on incorporating predictions from future climate. We describe a flexible, multivariate procedure for deriving such zones that incorporates a broad range of climatic characteristics while accounting for covariation among climate variables. With this approach, we derive provisional seed zones for four regions in the Desert Southwest (the Mojave Desert, Sonoran Desert, Colorado Plateau, and Southern Great Basin). To facilitate future‐resilient restoration designs, we project each zone into its relative position in the future climate based on near‐term, RCP4.5 and RCP8.5 emissions scenarios. Although provisional seed zones are useful in a variety of contexts, there are also situations in which site‐specific guidance is preferable. To meet this need, we implement Climate Distance Mapper, an interactive decision‐support tool designed to help practitioners match seed sources with restoration sites through an accessible online interface. The application allows users to rank the suitability of seed sources anywhere on the landscape based on multivariate climate distances. Users can perform calculations for either the current or future climates. Additionally, tools are available to guide sample effort in regional‐scale seed collections or to partition the landscape into climate clusters representing suitable planting sites for different seed sources. Our tools and analytic procedures represent a flexible and reproducible framework for advancing native plant development programs in the Desert Southwest and beyond.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.2453","usgsCitation":"Shryock, D., DeFalco, L., and Esque, T., 2018, Spatial decision‐support tools to guide restoration and seed‐sourcing in the Desert Southwest: Ecosphere, v. 9, no. 10, p. 1-19, https://doi.org/10.1002/ecs2.2453.","productDescription":"e02453; 19 p.","startPage":"1","endPage":"19","ipdsId":"IP-099467","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":468326,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.2453","text":"Publisher Index Page"},{"id":437721,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9R8YKL0","text":"USGS data release","linkHelpText":"Principal components of climate variation in the Desert Southwest (ver. 2.0, September 2019)"},{"id":437720,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FCRGHF","text":"USGS data release","linkHelpText":"Principal components of climate variation in the Desert Southwest for the time periods 1980-2010, 2040-2070 (RCP8.5) and (RCP4.5)"},{"id":358280,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.03662109374999,\n 31.27855085894653\n ],\n [\n -104.7216796875,\n 31.27855085894653\n ],\n [\n -104.7216796875,\n 42.01665183556825\n ],\n [\n -120.03662109374999,\n 42.01665183556825\n ],\n [\n -120.03662109374999,\n 31.27855085894653\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","issue":"10","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2018-10-05","publicationStatus":"PW","scienceBaseUri":"5bc02f70e4b0fc368eb5381d","contributors":{"authors":[{"text":"Shryock, Daniel F. 0000-0003-0330-9815 dshryock@usgs.gov","orcid":"https://orcid.org/0000-0003-0330-9815","contributorId":208659,"corporation":false,"usgs":true,"family":"Shryock","given":"Daniel F.","email":"dshryock@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":747970,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DeFalco, Lesley A. 0000-0002-7542-9261","orcid":"https://orcid.org/0000-0002-7542-9261","contributorId":208658,"corporation":false,"usgs":true,"family":"DeFalco","given":"Lesley A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":747969,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Esque, Todd 0000-0002-4166-6234 tesque@usgs.gov","orcid":"https://orcid.org/0000-0002-4166-6234","contributorId":195896,"corporation":false,"usgs":true,"family":"Esque","given":"Todd","email":"tesque@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":747971,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70209450,"text":"70209450 - 2018 - History and dynamics of the Greater Yellowstone Glacial System during the last two glaciations","interactions":[],"lastModifiedDate":"2020-04-08T12:18:02.608428","indexId":"70209450","displayToPublicDate":"2018-10-04T07:14:08","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3219,"text":"Quaternary Science Reviews","active":true,"publicationSubtype":{"id":10}},"title":"History and dynamics of the Greater Yellowstone Glacial System during the last two glaciations","docAbstract":"The Greater Yellowstone Glacial System (GYGS) covered about 20,000 km2 at its maximum Pleistocene extent. The initiation, culmination, and ultimate decay of the GYGS involved complex interactions between several coalescent ice masses flowing from glacial source areas adjoining and including the Yellowstone Plateau. Here, we present an updated review of the history and dynamics of the GYGS during the penultimate (Bull Lake) and last (Pinedale) glaciations, drawing upon an integration of glacial-geologic mapping with >130 cosmogenic 10Be and 3He exposure ages.\n\nBull Lake glacial deposits in greater Yellowstone are dated to ca. 150–140 ka and correlate with marine isotope stage 6. The Bull Lake glaciation extended well beyond the Pinedale along the southern and western GYGS margins, but Pinedale glaciers overrode Bull Lake ice limits on the north and east sides. The northeastward shift of the center of ice mass from Bull Lake to Pinedale time may be explained by uplift on the leading edge of the Yellowstone hotspot and subsidence on the trailing margin.\n\nIn early Pinedale time (∼22–18 ka), ice buildup culminated in the high terrain of the Beartooth Uplift and High Absaroka Range. Glaciers from these source regions flowed onto the northeastern margin of the Yellowstone Plateau and advanced to terminal moraines beyond Clarks Fork Canyon and in Jackson Hole. By middle Pinedale time (∼18–16 ka), the Yellowstone Plateau ice cap surface had risen above the equilibrium-line altitude, stimulating orographic glacial buildup nourished by storms funneled eastward through the Snake River Plain. The plateau ice cap eventually thickened to >1000 m and joined glaciers from the Beartooth Uplift and Gallatin Range to form the northern Yellowstone outlet glacier. Terrain east and downwind of the ice cap crest was placed in a precipitation shadow, resulting in glacial recession in these regions. During the late Pinedale (∼16–13 ka), the plateau ice cap prograded southwestward toward the direction of moisture supply, leading to advances along the southern and western margins of the GYGS. Northern sectors of the plateau ice cap were nearly stagnant at this time. The Yellowstone region experienced widespread deglaciation ca. 15–14 ka in response to warming climate. Unloading of the ∼1 km-thick plateau ice cap and consequent release of pressure on the magmatic system beneath Yellowstone was not accompanied by volcanism, indicating that the magma chamber was not primed to erupt via decompression during the last deglaciation.","language":"English","publisher":"Elsevier","doi":"10.1016/j.quascirev.2018.08.027","collaboration":"","usgsCitation":"Licciardi, J., and Pierce, K.L., 2018, History and dynamics of the Greater Yellowstone Glacial System during the last two glaciations: Quaternary Science Reviews, v. 200, p. 1-33, https://doi.org/10.1016/j.quascirev.2018.08.027.","productDescription":"33 p.","startPage":"1","endPage":"33","ipdsId":"IP-096424","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":468340,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.quascirev.2018.08.027","text":"Publisher Index Page"},{"id":373831,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Montana Wyoming","otherGeospatial":"Greater Yellowstone Glacial System","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.796875,\n 43.36512572875844\n ],\n [\n -108.8525390625,\n 43.36512572875844\n ],\n [\n -108.8525390625,\n 45.72152152227954\n ],\n [\n -111.796875,\n 45.72152152227954\n ],\n [\n -111.796875,\n 43.36512572875844\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"200","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Licciardi, Joseph M.","contributorId":223874,"corporation":false,"usgs":false,"family":"Licciardi","given":"Joseph M.","affiliations":[{"id":40784,"text":"Department of Earth Sciences, University of New Hampshire, 56 College Road, Durham, NH, 03824, joe.licciardi@unh.edu","active":true,"usgs":false}],"preferred":false,"id":786522,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pierce, Kenneth L. 0000-0002-2233-4015 kpierce@usgs.gov","orcid":"https://orcid.org/0000-0002-2233-4015","contributorId":223875,"corporation":false,"usgs":true,"family":"Pierce","given":"Kenneth","email":"kpierce@usgs.gov","middleInitial":"L.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":786523,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70199691,"text":"70199691 - 2018 - Burn severity controls on postfire Araucaria‐Nothofagus regeneration in the Andean Cordillera","interactions":[],"lastModifiedDate":"2018-11-14T09:17:48","indexId":"70199691","displayToPublicDate":"2018-09-25T16:29:25","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2193,"text":"Journal of Biogeography","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Burn severity controls on postfire Araucaria‐Nothofagus regeneration in the Andean Cordillera","title":"Burn severity controls on postfire Araucaria‐Nothofagus regeneration in the Andean Cordillera","docAbstract":"Aim
The aim of the study was to investigate postfire regeneration patterns of Araucaria‐Nothofagus forests on the west slope of the Andes; to evaluate the relationship between remotely sensed burn severity and forest mortality; and to assess controls of burn severity on forest response at local spatio‐temporal scales.
Location
Araucanía region in the western Andean Range of south‐central Chile where fire occurred during the 2001–2002 season.
Methods
Sampling of prefire stand structure and postfire vegetation response was performed along a burn severity gradient a decade after the fire. We evaluated the relationship between field‐measured tree mortality and satellite‐derived burn severity using a generalized linear model. We fit zero‐inflated mixture models to regeneration data of each genus to assess the importance of abiotic variables, stand characteristics, and biotic interactions.
Results
The relative version of the delta Normalized Burn Ratio explained 85% of the variability in canopy mortality. Nearly 12,000 hectares burned; the majority at high severity (67%). Regeneration densities of both genera were lower at higher levels of burn severity and higher with greater total basal area (live, dead, and down trees). The relative effect size of burn severity on regeneration was nearly twice as large for Nothofagus, which suggests information legacies of Araucaria have cascading effects on postdisturbance material legacies.
Main conclusions
Araucaria‐Nothofagus mortality from wildfire can be readily mapped using satellite‐derived burn severity. Although environmental site characteristics and biotic interactions mediate regeneration, basal area, and burn severity are the main mechanisms controlling regeneration. Forest refugia and postfire regeneration are vulnerable to recurrent fire. Therefore, we expect future fire (either increased severity or frequency), driven by landscape level changes, as a potential mechanism that can reduce local resilience of these forests as initial postfire material legacies (e.g., refugia and regeneration) are removed from the landscape. Our findings highlight an approach to quantify important attributes of forest disturbance and refugia, and identify areas for monitoring postdisturbance regeneration as the forests throughout south‐central Chile and Argentina face a multitude of potential change agents.
Climate and land‐use change are the major drivers of global biodiversity loss. Their effects are particularly acute for wide‐ranging consumers, but little is known about how these factors interact to affect the abundance of large carnivores and their herbivore prey. We analyzed population densities of a primary and secondary consumer (mule deer, Odocoileus hemionus, and mountain lion, Puma concolor) across a climatic gradient in western North America by combining satellite‐based maps of plant productivity with estimates of animal abundance and foraging area derived from Global Positioning Systems telemetry data (GPS). Mule deer density exhibited a positive, linear relationship with plant productivity (r2 = 0.58), varying by a factor of 18 across the climate‐vegetation gradient (range: 38–697 individuals/100 km2). Mountain lion home range size decreased in response to increasing primary productivity and consequent changes in the abundance of their herbivore prey (range: 20–450 km2). This pattern resulted in a strong, positive association between plant productivity and mountain lion density (r2 = 0.67). Despite varying densities, the ratio of prey to predator remained constant across the climatic gradient (mean ± SE = 363 ± 29 mule deer/mountain lion), suggesting that the determinacy of the effect of primary productivity on consumer density was conserved across trophic levels. As droughts and longer term climate changes reduce the suitability of marginal habitats, consumer home ranges will expand in order for individuals to meet basic nutritional requirements. These changes portend decreases in the abundance of large‐bodied, wide‐ranging wildlife through climatically driven reductions in carrying capacity, as well as increased human–wildlife interactions stemming from anthropogenic land use and habitat fragmentation.
","language":"English","publisher":"Wiley","doi":"10.1111/gcb.14364","usgsCitation":"Stoner, D.C., Sexton, J.O., Choate, D.M., Nagol, J., Bernales, H.H., Sims, S.A., Ironside, K.E., Longshore, K.M., and Edwards, T., 2018, Climatically driven changes in primary production propagate through trophic levels: Global Change Biology, v. 24, no. 10, p. 4453-4463, https://doi.org/10.1111/gcb.14364.","productDescription":"11 p.","startPage":"4453","endPage":"4463","ipdsId":"IP-090173","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":468414,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1463359","text":"Publisher Index Page"},{"id":357221,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Colorado Plateau, Great Basin, Mojave Desert","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.03662109374999,\n 32.80574473290688\n ],\n [\n -104.67773437499999,\n 32.80574473290688\n ],\n [\n -104.67773437499999,\n 42.049292638686836\n ],\n [\n -120.03662109374999,\n 42.049292638686836\n ],\n [\n -120.03662109374999,\n 32.80574473290688\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"24","issue":"10","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-08-07","publicationStatus":"PW","scienceBaseUri":"5b98a260e4b0702d0e842e42","contributors":{"authors":[{"text":"Stoner, David C.","contributorId":207777,"corporation":false,"usgs":false,"family":"Stoner","given":"David","email":"","middleInitial":"C.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":744695,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sexton, Joseph O.","contributorId":191918,"corporation":false,"usgs":false,"family":"Sexton","given":"Joseph","email":"","middleInitial":"O.","affiliations":[],"preferred":false,"id":744696,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Choate, David M.","contributorId":207778,"corporation":false,"usgs":false,"family":"Choate","given":"David","email":"","middleInitial":"M.","affiliations":[{"id":37455,"text":"University of Nevada","active":true,"usgs":false}],"preferred":false,"id":744697,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nagol, Jyothy","contributorId":207779,"corporation":false,"usgs":false,"family":"Nagol","given":"Jyothy","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":744698,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bernales, Heather H.","contributorId":198513,"corporation":false,"usgs":false,"family":"Bernales","given":"Heather","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":744699,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sims, Steven A.","contributorId":207780,"corporation":false,"usgs":false,"family":"Sims","given":"Steven","email":"","middleInitial":"A.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":744700,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ironside, Kirsten E. 0000-0003-1166-3793 kironside@usgs.gov","orcid":"https://orcid.org/0000-0003-1166-3793","contributorId":3379,"corporation":false,"usgs":true,"family":"Ironside","given":"Kirsten","email":"kironside@usgs.gov","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":744693,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Longshore, Kathleen M. 0000-0001-6621-1271 longshore@usgs.gov","orcid":"https://orcid.org/0000-0001-6621-1271","contributorId":2677,"corporation":false,"usgs":true,"family":"Longshore","given":"Kathleen","email":"longshore@usgs.gov","middleInitial":"M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":744694,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Edwards, Thomas C. Jr. 0000-0002-0773-0909 tce@usgs.gov","orcid":"https://orcid.org/0000-0002-0773-0909","contributorId":191916,"corporation":false,"usgs":true,"family":"Edwards","given":"Thomas C.","suffix":"Jr.","email":"tce@usgs.gov","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":false,"id":744692,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70198854,"text":"cir1447 - 2018 - U.S. Geological Survey energy and wildlife research annual report for 2018","interactions":[],"lastModifiedDate":"2018-12-12T09:35:36","indexId":"cir1447","displayToPublicDate":"2018-09-10T11:15:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":307,"text":"Circular","code":"CIR","onlineIssn":"2330-5703","printIssn":"1067-084X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1447","displayTitle":"U.S. Geological Survey Energy and Wildlife Research Annual Report for 2018","title":"U.S. Geological Survey energy and wildlife research annual report for 2018","docAbstract":"USGS scientists provide scientific information and options that land and resource managers and private industries can use to make decisions regarding the development of energy resources while protecting the health of ecosystems. Studies focus on delivering information to avoid, minimize, or mitigate the impacts of energy infrastructure on fish and wildlife. USGS scientists are currently developing mapping tools and models that identify areas of biological strengths and weaknesses or high- and low-quality habitat and can identify opportunities for conservation—areas of high-quality habitat where energy-generating potential is low—and areas of potential risk—areas of high-quality habitat where energy-generating potential is high. These tools can assist resource managers and the industry concerning siting of energy development and selection of off-site mitigation areas. Scientific efforts, such as these, further the understanding of impacts related to energy development and create workable solutions. The three goals guiding USGS activities related to the interactions between wildlife and energy development are to understand risks by identifying when, where, and how fish and wildlife share space with energy facilities, measure direct and indirect impacts to species, and inform feasible and cost-effective solutions to minimize impacts through technological fixes, management, and mitigation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1447","isbn":"978-1-4113-4257-6","collaboration":" ","usgsCitation":"Khalil, Mona, ed., 2018, U.S. Geological Survey energy and wildlife research annual report for 2018 (ver. 1.1, October 2018): U.S. Geological Survey Circular 1447, 102 p., https://doi.org/10.3133/cir1447.","productDescription":"v, 101 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-099243","costCenters":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"links":[{"id":357117,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1447/cir1447.pdf","text":"Report","size":"20.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"CIRC 1447"},{"id":357956,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/circ/1447/versionHist.txt","size":"1.32 KB","linkFileType":{"id":2,"text":"txt"}},{"id":357116,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1447/coverthb4.jpg"}],"edition":"Version 1.1: October 2018; Version 1.0: September 2018","contact":"Energy and Wildlife Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
The geologic map and accompanying report describes the extent, complexity, architecture, and evolution of the Bartlett Springs Fault Zone between Clear Lake and Round Valley, California. This fault zone is the eastern-most known active member of the San Andreas transform margin in northern California. It is of particular interest for its apparent long-lived history as a Miocene and older subduction-margin fault that, more recently, was reactivated as an active, creeping member of the San Andreas Fault system. The northern part of the Bartlett Springs Fault Zone is apparently still influenced by subduction of the Gorda Plate beneath North America, but it also accommodates strike-slip displacement associated with interaction of the Pacific Plate with North America. South of the map area, the Bartlett Springs Fault Zone steps into and merges with active faults of the eastern San Francisco Bay region; to the north of the map area and Round Valley, the fault zone steps into several other fault zones that connect with offshore thrust faults of the Cascadia subduction margin. Adequate understanding of the geologic framework of this fault zone and its relation to crustal structure of the adjacent region is important for purposes of planning and upgrading hydro-electric and other infrastructure in northern California that is directly or indirectly impacted by active faulting.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3395","collaboration":"Prepared in Cooperation with Pacific Gas and Electric Company","usgsCitation":"McLaughlin, R.J., Moring, B.C., Hitchcock, C.S., and Valin, Z.C., 2018, Framework geologic map and structure sections along the Bartlett Springs fault zone and adjacent area from Round Valley to Wilbur Springs, northern Coast Ranges, California (ver. 1.1, September 2018): U.S. Geological Survey Scientific Investigations Map 3395, 60 p., https://doi.org/10.3133/sim3395.","productDescription":"Pamphlet: iv, 60 p.; 2 Sheets: 39.22 x 46.34 inches and 42.00 x 54.69 inches; Databases; Metadata; Read Me","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-080509","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":399115,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_107702.htm","linkFileType":{"id":5,"text":"html"}},{"id":356586,"rank":9,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3395/sim3395_arcfiles","text":"Database Folder"},{"id":356583,"rank":8,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3395/metadata","text":"Metadata Folder"},{"id":356582,"rank":7,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3395/sim3395_v1.1_pamphlet.pdf","text":"Pamphlet","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3395"},{"id":356581,"rank":6,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3395/sim3395_arcfiles.zip","linkFileType":{"id":6,"text":"zip"},"description":"SIM 3395"},{"id":356579,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3395/sim3395_sheet1_v1.1.pdf","text":"Sheet 1","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3395"},{"id":356578,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3395/coverthb.jpg"},{"id":356357,"rank":2,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3395/sim3395_readme_BSFZ.txt","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3395 Read me"},{"id":358183,"rank":1,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sim/3395/versionHist.txt","linkFileType":{"id":2,"text":"txt"}},{"id":356580,"rank":5,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3395/sim3395_sheet2_v1.1.pdf","text":"Sheet 2","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3395"}],"scale":"100000","country":"United States","state":"California","otherGeospatial":"Bartlett Springs fault zone, northern Coast Ranges","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.375,\n 38.875\n ],\n [\n -122.25,\n 38.875\n ],\n [\n -122.25,\n 39.75\n ],\n [\n -123.375,\n 39.75\n ],\n [\n -123.375,\n 38.875\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: August 2018; Version 1.1: October 2018","contact":"Director,
Geology, Minerals, Energy, & Geophysics Science Center
Menlo Park, California
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591