Potential natural vegetation (PNV), the final successional stage of vegetation, plays a key role in ecological restoration, the design of nature reserves, and development of agriculture and livestock production. Meteorological data from historical and current periods including the last inter-glacial (LIG), last glacial maximum (LGM), mid Holocene (MH) periods and the present day (PD), plus derived data from 2050 and 2070, in conjunction with the Comprehensive and Sequential Classification System (CSCS) model, were used to classify global PNV. The 42 classes of global PNV were regrouped into 10 groups to facilitate analysis of spatial changes. Finally, spatio-temporal patterns and successional processes of global PNV as well as the response to climate changes were analysed. Our study made the following five conclusions. (1) Only one missing class (IA1 frigid-extrarid frigid desert, alpine desert) arose in periods of LIG, MH, 2050, and 2070 for global PNV. (2) The frigid-arid groups were mainly distributed in higher latitudes and elevations, but temperate-humid groups and tropical-perhumid groups occurred in middle and low latitudes, respectively. Temperate zonal forest steppe, warm desert, savanna and tropical zonal forest steppe increased, while six other groups decreased. (3) The conversion from temperate zonal forest steppe to tundra and alpine steppe from LIG to LGM occupied the largest area, indicating a drastic shift in climate and the associated response of terrestrial vegetation sensitive to climate change. (4) The CSCS could be used to simulate the long-term succession of global PNV. (5) As a consequence of global warming, forests shifted to the northern hemisphere and Tibet, areas with much higher latitude and elevation. The PNV groups with greater shift distance revealed the more serious effects of global climate change on vegetation.
Recent country and continental-scale digital soil mapping efforts have used a single model to predict soil properties across large regions. However, different ecophysiographic regions within large-extent areas are likely to have different soil-landscape relationships so models built specifically for these regions may more accurately capture these relationships relative to a ‘global’ model. We ask the question: Is a single ‘global’ model sufficient or are regionally-specific models useful for accurate digital soil mapping? We test this question by modeling soil depth classes across the 432,000 km2 upper Colorado River Basin in the Western USA using a single global model, multiple ecophysiographic models, and ensembles of the ecophysiographic models.
Effective soil depth class observations (n = 12,194) were derived from multiple soil databases. Fifty-seven environmental covariates were derived from a 30 m digital elevation model, climate data, satellite imagery, and aeroradiometric data. Three independent land classifications were used to stratify the area. Two expert-derived land classifications, USDA Major Land Resource Areas (MLRA) and US-EPA Level III ecoregions, divided the study area into multiple ecophysiographic regions based on vegetation and broad-scale physiographic differences. The third land classification divided the study area into broad landforms.
Soil depth observations were split into separate training (n = 10,470) and validation (n = 1,724) datasets. First, a ‘global’ random forest model was used to model soil depth classes using all training observations and covariates. ‘Global’ denotes a model built with all training data across the extent of the area, not a model at world extent. Second, the land classifications were used to subset the observations into ecophysiographic sub-datasets and random forest models were refit for each region. Models fit by ecophysiographic region are referred to as regional models. Thirdly, predictions from each regional model were fused into regional-ensemble models. Accuracy, Brier scores, and Shannon’s entropy were used to compare model accuracy and uncertainty. Regional ecophysiographic models were also compared to models built for geographic areas that were defined solely to be approximately equal in area. Training dataset density and the imbalance ratio were investigated to determine if data characteristics influenced regional accuracy/uncertainty metrics.
Accuracy for the global model using the validation set was 62.8%. Regional model accuracies ranged between 56.1% and 75.0%. We found: 1) useful inter-regional differences in global model accuracy were revealed when the global model was validated by region, 2) no consistent relationship between training observation density and accuracy/uncertainty metrics, 3) no meaningful differences in accuracy and uncertainty metrics between physiographic and geographic regions, 4) ensembles of regionally-specific models were approximately as accurate as global models, and 5) both region-specific models and ensembles of regional models were less uncertain than the global model. Overall, we recommend the use of soil depth class predictions made from MLRA regional ensemble models because this prediction had higher accuracy than the ecoregion ensemble model prediction, but lower uncertainty than both the global model and the landform ensemble model predictions. We answer our question: Ensembles of regionally-specific models are approximately as accurate as global models, but result in less uncertainty.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.geoderma.2021.114998","usgsCitation":"Brungard, C.C., Nauman, T.W., Duniway, M.C., Veblen, K.E., Nehring, K.C., White, D.S., Salley, S.W., and Anchang, J., 2021, Regional ensemble modeling reduces uncertainty for digital soil mapping: Geoderma, v. 397, 114998, 15 p., https://doi.org/10.1016/j.geoderma.2021.114998.","productDescription":"114998, 15 p.","ipdsId":"IP-124150","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":452871,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.geoderma.2021.114998","text":"Publisher Index Page"},{"id":388990,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Colorado, Nevada, New Mexico, Utah, Wyoming","otherGeospatial":"Colorado River Basin","geographicExtents":"{\n \"type\": 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]\n}","volume":"397","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Brungard, Colby C.","contributorId":248822,"corporation":false,"usgs":false,"family":"Brungard","given":"Colby","email":"","middleInitial":"C.","affiliations":[{"id":50029,"text":"New Mexico State University, Department of Plant and Environmental Sciences, Las Cruces, NM","active":true,"usgs":false}],"preferred":false,"id":822824,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nauman, Travis W. 0000-0001-8004-0608 tnauman@usgs.gov","orcid":"https://orcid.org/0000-0001-8004-0608","contributorId":169241,"corporation":false,"usgs":true,"family":"Nauman","given":"Travis","email":"tnauman@usgs.gov","middleInitial":"W.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":822825,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":4212,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":822826,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Veblen, Kari E.","contributorId":76872,"corporation":false,"usgs":false,"family":"Veblen","given":"Kari","email":"","middleInitial":"E.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":822827,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nehring, Kyle C.","contributorId":210415,"corporation":false,"usgs":false,"family":"Nehring","given":"Kyle","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":822828,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"White, David S.","contributorId":173069,"corporation":false,"usgs":false,"family":"White","given":"David","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":822829,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Salley, Shawn W.","contributorId":216783,"corporation":false,"usgs":false,"family":"Salley","given":"Shawn","email":"","middleInitial":"W.","affiliations":[{"id":39514,"text":"USDA-Agricultural Resource Service, Jornada Experimental Range, Las Cruces, NM 88003, USA","active":true,"usgs":false}],"preferred":false,"id":822830,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Anchang, Julius","contributorId":265510,"corporation":false,"usgs":false,"family":"Anchang","given":"Julius","email":"","affiliations":[{"id":54703,"text":"Department of Plant and Environmental Sciences, New Mexico State University, Las Cruces, NM 88003","active":true,"usgs":false}],"preferred":false,"id":822831,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70219201,"text":"ofr20201154 - 2021 - Range-wide greater sage-grouse hierarchical monitoring framework—Implications for defining population boundaries, trend estimation, and a targeted annual warning system","interactions":[],"lastModifiedDate":"2021-03-31T11:34:59.149189","indexId":"ofr20201154","displayToPublicDate":"2021-03-30T10:32:04","publicationYear":"2021","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":"2020-1154","displayTitle":"Range-wide Greater Sage-Grouse Hierarchical Monitoring Framework: Implications for Defining Population Boundaries, Trend Estimation, and a Targeted Annual Warning System","title":"Range-wide greater sage-grouse hierarchical monitoring framework—Implications for defining population boundaries, trend estimation, and a targeted annual warning system","docAbstract":"Incorporating spatial and temporal scales into greater sage-grouse (Centrocercus urophasianus) population monitoring strategies is challenging and rarely implemented. Sage-grouse populations experience fluctuations in abundance that lead to temporal oscillations, making trend estimation difficult. Accounting for stochasticity is critical to reliably estimate population trends and investigate variation related to deterministic factors on the landscape, which are amenable to management action. Here, we describe a novel, range-wide hierarchical monitoring framework for sage-grouse centered on four objectives: (1) create a standardized database of lek counts, (2) develop spatial population structures by clustering leks, (3) estimate spatial trends at different temporal extents based on abundance nadirs (troughs), and (4) develop a targeted annual warning system to help inform management decisions. Using automated and repeatable methods (software), we compiled a lek database (as of 2019) that contained 262,744 counts and 8,421 unique lek locations from disparate state data. The hierarchical population units (clusters) included 13 nested levels, identifying biologically relevant units and population structure that minimized inter-cluster sage-grouse movements. With these products, we identified spatiotemporal variation in trends in population abundance using Bayesian state-space models. We estimated 37.0, 65.2, and 80.7-percent declines in abundance range-wide during short (17 years), medium (33 years), and long (53 years) temporal scales, respectively. However, some areas exhibited evidence of increasing trends in abundance in recent decades. Models predicted 12.3, 19.2, and 29.6 percent of populations (defined as clusters of neighboring leks) consisted of over 50-percent probability of extirpation at 19, 38, and 56-year projections from 2019, respectively, based on averaged annual rate of change in apparent abundance across two, four, and six oscillations (average period of oscillation is 9.4 years). At the lek level, models predicted 45.7, 60.1, and 78.0 percent of leks with over 50-percent extirpation probabilities over the same time periods, respectively, mostly located on the periphery of the species’ range. The targeted annual warning system automates annual identification of local populations exhibiting asynchronous decline relative to regional population patterns using simulated management actions and an optimization algorithm for evaluating range-wide stabilization of population abundance. In 2019, approximately 3.2 percent of leks and 2.0 percent of populations were identified by the targeted annual warning system for management intervention range-wide.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201154","collaboration":"Prepared in cooperation with the Western Association of Fish and Wildlife Agencies and the Bureau of Land Management","usgsCitation":"Coates, P.S., Prochazka, B.G., O’Donnell, M.S., Aldridge, C.L., Edmunds, D.R., Monroe, A.P., Ricca, M.A., Wann, G.T., Hanser, S.E., Wiechman, L.A., and Chenaille, M.P., 2021, Range-wide greater sage-grouse hierarchical monitoring framework—Implications for defining population boundaries, trend estimation, and a targeted annual warning system: U.S. Geological Survey Open-File Report 2020–1154, 243 p., https://doi.org/10.3133/ofr20201154.","productDescription":"Report: vi, 243 p.; 1 Table","numberOfPages":"243","onlineOnly":"Y","ipdsId":"IP-123421","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":384766,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2020/1154/ofr20201154_table8.csv","text":"Table 8","size":"80 KB","linkFileType":{"id":7,"text":"csv"}},{"id":384765,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2020/1154/ofr20201154_table8.xlsx","text":"Table 8","size":"60 KB","linkFileType":{"id":3,"text":"xlsx"}},{"id":384760,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1154/ofr20201154.pdf","text":"Report","size":"310 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":384759,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1154/covrthb.jpg"}],"country":"United States","state":"California, Colorado, Idaho, Montana, Nevada, North Dakota, Oregon, South Dakota, Utah, Washington, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.32226562500001,\n 35.67514743608467\n ],\n [\n -103.447265625,\n 35.67514743608467\n ],\n [\n -103.447265625,\n 48.69096039092549\n ],\n [\n -120.32226562500001,\n 48.69096039092549\n ],\n [\n -120.32226562500001,\n 35.67514743608467\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
This document presents the renewed vision, mission, and goals for the National Cooperative Geologic Mapping Program (NCGMP). The NCGMP, as authorized by the National Cooperative Geologic Mapping Act of 1992 (Public Law 102-285, 106 Stat. 166 and its reauthorizations), is tasked with expediting the production of a geologic database for the Nation based on modern geologic maps and their supporting data. In addition to highlighting the benefits of geologic maps for economic prosperity, national security, and environmental quality, the report describes the NCGMP structure and components. A renewed vision and mission for the NCGMP are stated, and three goals for guiding the program toward that vision for the next ten years are established. The vision of creating an integrated, three-dimensional, digital geologic map of the United States and its territories to address the changing needs of the Nation by 2030 is thereby defined to drive the activities of all NCGMP components for the next ten years. The strategic actions required to realize the NCGMP vision are identified for each of its components.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211013","usgsCitation":"Brock, J., Berry, K., Faulds, J., Berg, R., House, K., Marketti, M., McPhee, D., Schmidt, K., Schmitt, J., Soller, D., Spears, D., Thompson, R., Thorleifson, H., and Walsh, G., 2021, Renewing the National Cooperative Geologic Mapping Program as the Nation’s authoritative source for modern geologic knowledge: U.S. Geological Survey Open-File Report 2021–1013, 10 p., https://doi.org/10.3133/ofr20211013.","productDescription":"vi, 10 p.","numberOfPages":"10","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-115733","costCenters":[{"id":412,"text":"National Cooperative Geologic Mapping Program","active":false,"usgs":true}],"links":[{"id":384680,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1013/ofr20211013.pdf","text":"Report","size":"0.97 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1013"},{"id":384679,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1013/coverthb.jpg"}],"contact":"National Cooperative Geologic Mapping Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
The Global Seismographic Network (GSN) is a multiuse, globally distributed seismic network used by seismologists, to both characterize earthquakes and study the Earth’s interior. Most stations in the network have two collocated broadband seismometers, which enable network operators to identify potential metadata and sensor issues. In this study, we investigate the accuracy with which surface waves can be measured across the GSN, by comparing waveforms of vertical‐component Rayleigh waves from
The virus that causes COVID‐19 likely evolved in a mammalian host, possibly Old‐World bats, before adapting to humans, raising the question of whether reverse zoonotic transmission to bats is possible. Wildlife management agencies in North America are concerned that the activities they authorize could lead to transmission of SARS‐CoV‐2 to bats from humans. A rapid risk assessment conducted in April 2020 suggested that there was a small but significant possibility that SARS‐CoV‐2 could be transmitted from humans to bats during summer fieldwork, absent precautions. Subsequent challenge studies in a laboratory setting have shed new information on these risks, as has more detailed information on human epidemiology and transmission. This inquiry focuses on the risk to bats from winter fieldwork, specifically surveys of winter roosts and handling of bats to test for white‐nose syndrome or other research needs. We use an aerosol transmission model, with parameter estimates both from the literature and from formal expert judgment, to estimate the risk to three species of North American bats, as a function of several factors. We find that risks of transmission are lower than in the previous assessment and are notably affected by chamber volume and local prevalence of COVID‐19. Use of facemasks with high filtration efficiency or a negative COVID‐19 test before field surveys can reduce zoonotic risk by 65 to 88%.
","language":"English","publisher":"Wiley","doi":"10.1111/csp2.410","usgsCitation":"Cook, J., Campbell Grant, E.H., Coleman, J.T., Sleeman, J.M., and Runge, M.C., 2021, Risks posed by SARS‐CoV‐2 to North American bats during winter fieldwork: Conservation Science and Practice, v. 3, no. 6, e410, 17 p., https://doi.org/10.1111/csp2.410.","productDescription":"e410, 17 p.","ipdsId":"IP-125933","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":452891,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/csp2.410","text":"Publisher Index Page"},{"id":436429,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QAID7C","text":"USGS data release","linkHelpText":"Decision-Support Tool to Estimate SARS-CoV-2 Human-to-bat Transmission Risk"},{"id":384893,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"3","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-03-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Cook, Jonathan D","contributorId":256954,"corporation":false,"usgs":false,"family":"Cook","given":"Jonathan D","affiliations":[{"id":24700,"text":"Student contractor","active":true,"usgs":false}],"preferred":false,"id":813577,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Campbell Grant, Evan H. 0000-0003-4401-6496 ehgrant@usgs.gov","orcid":"https://orcid.org/0000-0003-4401-6496","contributorId":150443,"corporation":false,"usgs":true,"family":"Campbell Grant","given":"Evan","email":"ehgrant@usgs.gov","middleInitial":"H.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":813578,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Coleman, Jeremy T. H.","contributorId":256955,"corporation":false,"usgs":false,"family":"Coleman","given":"Jeremy","email":"","middleInitial":"T. H.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":813579,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sleeman, Jonathan M. 0000-0002-9910-6125 jsleeman@usgs.gov","orcid":"https://orcid.org/0000-0002-9910-6125","contributorId":128,"corporation":false,"usgs":true,"family":"Sleeman","given":"Jonathan","email":"jsleeman@usgs.gov","middleInitial":"M.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":82110,"text":"Midcontinent Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":813580,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Runge, Michael C. 0000-0002-8081-536X mrunge@usgs.gov","orcid":"https://orcid.org/0000-0002-8081-536X","contributorId":3358,"corporation":false,"usgs":true,"family":"Runge","given":"Michael","email":"mrunge@usgs.gov","middleInitial":"C.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":813581,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70219172,"text":"ds1136 - 2021 - Groundwater-quality and select quality-control data from the National Water-Quality Assessment Project, January 2017 through December 2019","interactions":[],"lastModifiedDate":"2021-03-30T11:57:07.918866","indexId":"ds1136","displayToPublicDate":"2021-03-29T17:42:50","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1136","displayTitle":"Groundwater-Quality and Select Quality-Control Data from the National Water-Quality Assessment Project, January 2017 through December 2019","title":"Groundwater-quality and select quality-control data from the National Water-Quality Assessment Project, January 2017 through December 2019","docAbstract":"Groundwater-quality environmental data were collected from 983 wells as part of the National Water-Quality Assessment Project of the U.S. Geological Survey National Water Quality Program and are included in this report. The data were collected from six types of well networks: principal aquifer study networks, which are used to assess the quality of groundwater used for public water supply; land-use study networks, which are used to assess land-use effects on shallow groundwater quality; major aquifer study networks, which are used to assess the quality of groundwater used for domestic supply; enhanced trends networks, which are used to evaluate the time scales during which groundwater quality changes; vertical flow-path study networks, which are used to evaluate changes in groundwater quality from shallow to deeper depths; and modeling support studies, which are used to provide data to support groundwater modeling. Groundwater samples were analyzed for many water-quality indicators and constituents, including major ions, nutrients, trace elements, volatile organic compounds, pesticides, radionuclides, microbiological indicators, and some constituents of special interest (arsenic speciation, hexavalent chromium [chromium (VI)], and perchlorate). These groundwater-quality data, along with data from quality-control samples, are tabulated in this report and in an associated data release. Data for microbiological indicators for samples collected in 2016 are included in the companion data release.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1136","collaboration":"National Water-Quality Assessment Project","usgsCitation":"Kingsbury, J.A., Bexfield, L.M., Arnold, T., Musgrove, M., Erickson, M.L., Degnan, J.R., Tesoriero, A.J., Lindsey, B.D., and Belitz, K., 2021, Groundwater-quality and select quality-control data from the National Water-Quality Assessment Project, January 2017 through December 2019: U.S. Geological Survey Data Series 1136, 97 p., https://doi.org/10.3133/ds1136.","productDescription":"Report: x, 97 p.; 2 Appendixes; Data Release; Dataset","numberOfPages":"112","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-118835","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":583,"text":"Texas Water Science 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U.S. Geological Survey
640 Grassmere Park Drive
Nashville, TN 37211
This study investigates sedimentary and geomorphic processes in eastern Chuckwalla Valley, Riverside County, California, a region of arid, basin-and-range terrain where extensive solar-energy development is planned. The objectives of this study were to (1) measure local weather parameters and use them to model aeolian sediment-transport potential; (2) identify surface sedimentary characteristics in representative localities; and (3) evaluate long-term landscape evolution rates and processes by analyzing stratigraphy in combination with luminescence geochronology.
The new stratigraphic and geochronologic data presented in this report demonstrate the varying local significance of aeolian, alluvial fan, lacustrine (playa), and possibly Colorado River influence over a range of time scales. The dominant sand-transport direction in eastern Chuckwalla Valley is toward the northeast, consistent with the recognized regional west-to-east wind direction. However, occasional strong wind events from the north can transport large quantities of sand southward and temporarily reshape local geomorphic features. Influence of a northwest wind direction is also locally dominant around mountain ranges and controls the modern morphology of the Palen dune field. Modeled sand fluxes are on the order of 105 kilograms per meter width per year at the site of weather monitoring, 5 kilometers northwest of the Mule Mountains. Aeolian dunes are locally well developed and actively migrating. Their location and activity are determined largely by sediment supply from playa surfaces and ephemeral stream channels, which also control the dunes’ spatial extent and migration potential; stream channels act as both source and sink for aeolian sediment in this environment.
Excavations at five sites along a northwest-to-southeast transect reveal that playa deposits formed around 266–226 thousand years ago south of the McCoy Mountains and immediately north of the present location of Interstate 10. The playa material is overlain by late Pleistocene to Holocene alluvial fan deposits. To the southeast (south of Interstate 10, but north of the Mule Mountains), we identified rapid accumulation of alluvial sediment around the time of the Last Glacial Maximum (23–20 thousand years ago), unconformably overlain by a locally varying assemblage of recent aeolian material or Holocene alluvial fan sediment. We have used stratigraphic characteristics and luminescence ages to calculate accumulation rates for sites in eastern Chuckwalla Valley, and thereby to identify spatial variation in landscape stability over decadal and longer time scales.
If future solar-energy development plans are to include natural sand-transport corridors, plans would entail retaining the ability for sand to be transported eastward from the ephemeral stream channels and playas that supply sediment to the dunes, sand sheets, and sand ramps of Chuckwalla Valley, and also to allow for southward transport during episodic strong weather events several times per year. The aeolian sediment-transport corridors are dynamic spatially and temporally, reorganizing on the basis of seasonal changes to wind drift potential. Future landscape stability also will be determined by climate-driven changes to vegetation and thereby to aeolian sediment availability. In a warmer, drier climate, aeolian sediment activity is expected to increase, owing to a decrease in stabilizing vegetation cover and more extreme rain that supplies sediment to ephemeral stream channels and playas from which it is remobilized by wind.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215017","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"East, A.E., Gray, H.J., Redsteer, M.H., and Ballmer, M., 2021, Landscape evolution in eastern Chuckwalla Valley, Riverside County, California: U.S. Geological Survey Scientific Investigations Report 2021–5017, 46 p., https://doi.org/10.3133/sir20215017.","productDescription":"Report: vi, 46 p.; Data Release","numberOfPages":"36","onlineOnly":"Y","ipdsId":"IP-124276","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science 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Coastal and Marine Science Center
U.S. Geological Survey
Pacific Coastal and Marine Science Center
2885 Mission St.
Santa Cruz, CA 95060
Multidisciplinary approaches to conservation and wildlife management are often effective in addressing complex, multi-factor problems. Emerging fields such as conservation physiology and conservation behaviour can provide innovative solutions and management strategies for target species and systems. Sensory ecology combines the study of ‘how animals acquire’ and process sensory stimuli from their environments, and the ecological and evolutionary significance of ‘how animals respond’ to this information. We review the benefits that sensory ecology can bring to wildlife conservation and management by discussing case studies across major taxa and sensory modalities. Conservation practices informed by a sensory ecology approach include the amelioration of sensory traps, control of invasive species, reduction of human–wildlife conflicts and relocation and establishment of new populations of endangered species. We illustrate that sensory ecology can facilitate the understanding of mechanistic ecological and physiological explanations underlying particular conservation issues and also can help develop innovative solutions to ameliorate conservation problems.
","language":"English","publisher":"Oxford University Press","doi":"10.1093/conphys/coab002","usgsCitation":"Elmer, L.K., Madliger, C.L., Blumstein, D.T., Elvidge, C.K., Fernandex-Juricic, E., Horodysky, A.Z., Johnson, N.S., McGuire, L.P., Swaisgood, R.R., and Cooke, S., 2021, Exploiting common senses: Sensory ecology meets wildlife conservation and management: Conservation Physiology, v. 9, no. 1, coab002, 29 p., https://doi.org/10.1093/conphys/coab002.","productDescription":"coab002, 29 p.","ipdsId":"IP-123988","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":452893,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/conphys/coab002","text":"Publisher Index Page"},{"id":387851,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-03-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Elmer, Laura K","contributorId":264140,"corporation":false,"usgs":false,"family":"Elmer","given":"Laura","email":"","middleInitial":"K","affiliations":[{"id":17786,"text":"Carleton University","active":true,"usgs":false}],"preferred":false,"id":820955,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Madliger, Christine L","contributorId":264141,"corporation":false,"usgs":false,"family":"Madliger","given":"Christine","email":"","middleInitial":"L","affiliations":[{"id":17786,"text":"Carleton University","active":true,"usgs":false}],"preferred":false,"id":820956,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Blumstein, Daniel T.","contributorId":150453,"corporation":false,"usgs":false,"family":"Blumstein","given":"Daniel","email":"","middleInitial":"T.","affiliations":[{"id":18023,"text":"Ecology and Evolutionary Biology, UCLA","active":true,"usgs":false}],"preferred":false,"id":820957,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Elvidge, Chris K","contributorId":264142,"corporation":false,"usgs":false,"family":"Elvidge","given":"Chris","email":"","middleInitial":"K","affiliations":[{"id":17786,"text":"Carleton University","active":true,"usgs":false}],"preferred":false,"id":820958,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fernandex-Juricic, Esteban","contributorId":264143,"corporation":false,"usgs":false,"family":"Fernandex-Juricic","given":"Esteban","email":"","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":820959,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Horodysky, Andrij Z","contributorId":264144,"corporation":false,"usgs":false,"family":"Horodysky","given":"Andrij","email":"","middleInitial":"Z","affiliations":[{"id":54388,"text":"Hampton University","active":true,"usgs":false}],"preferred":false,"id":820960,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Johnson, Nicholas S. 0000-0002-7419-6013 njohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-7419-6013","contributorId":597,"corporation":false,"usgs":true,"family":"Johnson","given":"Nicholas","email":"njohnson@usgs.gov","middleInitial":"S.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":820961,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"McGuire, Liam P","contributorId":264145,"corporation":false,"usgs":false,"family":"McGuire","given":"Liam","email":"","middleInitial":"P","affiliations":[{"id":6655,"text":"University of Waterloo","active":true,"usgs":false}],"preferred":false,"id":820962,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Swaisgood, Ronald R.","contributorId":69490,"corporation":false,"usgs":false,"family":"Swaisgood","given":"Ronald","email":"","middleInitial":"R.","affiliations":[{"id":12762,"text":"San Diego Zoo Institure for Conservation Research","active":true,"usgs":false}],"preferred":false,"id":820963,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Cooke, Steven J.","contributorId":56132,"corporation":false,"usgs":false,"family":"Cooke","given":"Steven J.","affiliations":[{"id":36574,"text":"Carleton University, Ottawa, Ontario","active":true,"usgs":false}],"preferred":false,"id":820964,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70220474,"text":"70220474 - 2021 - Optimal allocation of law enforcement patrol effort to mitigate poaching activities","interactions":[],"lastModifiedDate":"2021-08-03T15:19:58.956481","indexId":"70220474","displayToPublicDate":"2021-03-29T07:27:47","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Optimal allocation of law enforcement patrol effort to mitigate poaching activities","docAbstract":"Poaching is a global problem causing the decline of species worldwide. Optimizing the efficiency of ranger patrols to deter poaching activity at the lowest possible cost is crucial for protecting species with limited resources. We applied decision analysis and spatial optimization algorithms to allocate efforts of ranger patrols throughout a national park. Our objective was to mitigate poaching activity at or below management risk targets for the lowest monetary cost. We examined this trade‐off by constructing a Pareto efficiency frontier using integer linear programming. We used data from a ranger‐based monitoring program in Nyungwe National Park, Rwanda. Our measure of poaching risk is based on dynamic occupancy models that account for imperfect detection of poaching activities. We found that in order to achieve a 5% reduction in poaching risk, 622 ranger patrol events (each corresponding to patrolling 1‐km2 sites) were needed within a year at a cost of US$49,760. In order to attain a 60% reduction in poaching risk, 15,560 patrol events were needed at a cost of US\\$1,244,800. We evaluated the trade‐off between patrol cost and poaching risk based on our model by constructing a Pareto efficiency frontier and park managers found the solution for a 50% risk reduction to be a practical trade‐off based on funding constraints (comparable to recent years) and the diminishing returns between risk mitigation and cost. This expected reduction in risk required 8,558 patrol events per year at a cost of US \\$684,640. Our results suggest that optimal solutions could increase efficiency compared to the actual effort allocations from 2006 to 2016 in Nyungwe National Park (e.g., risk reductions of ~30% under recent budgets compared to ~50% reduction in risk under the optimal strategy). The modeling framework in this study took into account imperfect detection of poaching risk as well as the directional and conditional nature of ranger patrol events given the spatial adjacency relationships of neighboring sites and access points. Our analyses can help to improve the efficiency of ranger patrols, and the modeling framework can be broadly applied to other spatial conservation planning problems with conditional, multilevel, site selection.
The extent and frequency of fire has increased in many arid systems over the last century, with a large proportion of area in some regions undergoing transitions to novel conditions. Portions of the Mojave Desert in southwestern North America have undergone such transitions, most often from woody to herbaceous-dominated systems. These transitions have often been attributed to the proliferation of invasive annual grasses that promote more frequent fire, but recent evidence indicates that transitions can also occur independent of fire frequency if burn severity is high. In addition, high probability of ignition (i.e., potentially high fire frequency) and high burn severity may not always be geographically related. Therefore, our goals were to: (1) map potential burn severity, fire frequency, and probability of ignition across the Mojave; and, (2) evaluate spatial association among predicted burn severity, fire frequency and probability of ignition. We first mapped perimeters of 250 wildfires > 405 ha that occurred from 1972 to 2010, then extracted data on fire frequency (number of times burned from 1972 to 2010), burn severity (the difference Normalized Burn Ratio), and 15 predictor variables representing physiography, climate, ignition, and vegetation. Maximum entropy was used to predict probability of ignition and Random Forest models were used to predict dNBR and fire frequency. Areas with high burn severity and high ignition probability had opposite spatial trends; areas with high burn severity were predicted to predominantly be in the northwest part of the region whereas areas with high ignition probability were predicted to be in the northeast. The models indicate the existence of a number of spatially structured but temporally dynamic fire regimes throughout the Mojave Desert. Two prevalent and ecologically significant regimes include one with frequent fires of low to moderate severity and another with infrequent fire of high severity. Areas with high fire frequency are currently limited in extent (<1% total area). However, cover of invasive grasses can remain high decades after a burn of high or moderate severity, so grass-fire cycles could develop in areas where there may be expectations of infrequent fire as well as those with relatively high fire frequency.
While wetlands are the largest natural source of methane (CH4) to the atmosphere, they represent a large source of uncertainty in the global CH4 budget due to the complex biogeochemical controls on CH4 dynamics. Here we present, to our knowledge, the first multi-site synthesis of how predictors of CH4 fluxes (FCH4) in freshwater wetlands vary across wetland types at diel, multiday (synoptic), and seasonal time scales. We used several statistical approaches (correlation analysis, generalized additive modeling, mutual information, and random forests) in a wavelet-based multi-resolution framework to assess the importance of environmental predictors, nonlinearities and lags on FCH4 across 23 eddy covariance sites. Seasonally, soil and air temperature were dominant predictors of FCH4 at sites with smaller seasonal variation in water table depth (WTD). In contrast, WTD was the dominant predictor for wetlands with smaller variations in temperature (e.g., seasonal tropical/subtropical wetlands). Changes in seasonal FCH4 lagged fluctuations in WTD by ~17 ± 11 days, and lagged air and soil temperature by median values of 8 ± 16 and 5 ± 15 days, respectively. Temperature and WTD were also dominant predictors at the multiday scale. Atmospheric pressure (PA) was another important multiday scale predictor for peat-dominated sites, with drops in PA coinciding with synchronous releases of CH4. At the diel scale, synchronous relationships with latent heat flux and vapor pressure deficit suggest that physical processes controlling evaporation and boundary layer mixing exert similar controls on CH4 volatilization, and suggest the influence of pressurized ventilation in aerenchymatous vegetation. In addition, 1- to 4-h lagged relationships with ecosystem photosynthesis indicate recent carbon substrates, such as root exudates, may also control FCH4. By addressing issues of scale, asynchrony, and nonlinearity, this work improves understanding of the predictors and timing of wetland FCH4 that can inform future studies and models, and help constrain wetland CH4 emissions.
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statistical approaches have greatly expanded the breadth of knowledge regarding aquatic ecosystem metabolism - the measurement and interpretation of gross primary productivity (GPP) and ecosystem respiration (ER). Aquatic scientists are poised to take advantage of widely available datasets and freely-available modeling tools to apply functional information gained through ecosystem metabolism to better environmental management. Historically, several logistical and conceptual factors have limited the widespread application of metabolism in management settings. Benefitting from new instrumental and modeling tools, it is now relatively straightforward to extend routine monitoring of dissolved oxygen (DO) to dynamic measures of aquatic ecosystem function (GPP & ER) and key physical processes such as gas exchange with the atmosphere (G). We review the current approaches for using DO data in environmental management with a focus on the United States, but briefly describe management frameworks in Europe and Canada. We highlight new applications of diel DO data and metabolism in regulatory settings and explore how they can be applied to managing and monitoring ecosystems. We then review existing data types and provide a short guide for implementing field measurements and modeling of ecosystem metabolic processes using currently available tools. Finally, we discuss research needed to overcome current conceptual limitations of applying metabolism in management settings. Despite challenges associated with modeling metabolism in rivers and lakes, rapid developments in this field have moved us closer to utilizing real-time estimates of GPP, ER and G to improve the assessment and management of environmental change.","language":"English","publisher":"Wiley","doi":"10.1002/wat2.1521","usgsCitation":"Jankowski, K.J., Mejia, F.H., Blaszczak, J., and Holtgrieve, G.W., 2021, Aquatic ecosystem metabolism as a tool in environmental management: WIREs Water, v. 8, no. 4, e1521, 27 p., https://doi.org/10.1002/wat2.1521.","productDescription":"e1521, 27 p.","ipdsId":"IP-122380","costCenters":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":387577,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-03-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Jankowski, Kathi Jo 0000-0002-3292-4182","orcid":"https://orcid.org/0000-0002-3292-4182","contributorId":207429,"corporation":false,"usgs":true,"family":"Jankowski","given":"Kathi","email":"","middleInitial":"Jo","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":820304,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mejia, Francine H. 0000-0003-4447-231X","orcid":"https://orcid.org/0000-0003-4447-231X","contributorId":214345,"corporation":false,"usgs":true,"family":"Mejia","given":"Francine","email":"","middleInitial":"H.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":820305,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Blaszczak, Joanna 0000-0001-5122-0829","orcid":"https://orcid.org/0000-0001-5122-0829","contributorId":225159,"corporation":false,"usgs":false,"family":"Blaszczak","given":"Joanna","email":"","affiliations":[{"id":41055,"text":"Natural Resources and Environmental Science, University of Nevada, Reno, NV 89557, USA","active":true,"usgs":false}],"preferred":false,"id":820306,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Holtgrieve, Gordon W. 0000-0002-4451-3567","orcid":"https://orcid.org/0000-0002-4451-3567","contributorId":213257,"corporation":false,"usgs":false,"family":"Holtgrieve","given":"Gordon","email":"","middleInitial":"W.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":820307,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70219179,"text":"70219179 - 2021 - GIS object data properties","interactions":[],"lastModifiedDate":"2021-03-30T12:48:38.16444","indexId":"70219179","displayToPublicDate":"2021-03-28T07:47:39","publicationYear":"2021","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"GIS object data properties","docAbstract":"Data properties are characteristics of GIS attribute systems and values whose design and format impacts analytical and computational processing. Geospatial data are expressed at conceptual, logical, and physical levels of database abstraction intended to represent geographical information. The appropriate design of attribute systems and selection of properties should be logically consistent and support appropriate scales of measurement for representation and analysis. Geospatial concepts such as object-field views and dimensional space for relating objects and qualities form data models based on a geographic matrix and feature geometry. Three GIS approaches and their attribute system design are described: tessellations, vectors, and graphs.
Rapid phenotypic stock identification in mixed-stock fisheries can provide a useful alternative to more time-intensive methods (e.g., coded wire tags, genetics) in assessing harvest and informing management decisions. We leveraged local ecological knowledge, existing stock identification methods, and understanding of life history differences to develop rapid stock identification tools for fall-run Chinook Salmon Oncorhynchus tshawytscha encountered in the Buoy 10 recreational fishery at the mouth of the Columbia River. Specifically, we sought to differentiate between the fishery’s two dominant genetic lineages: lower river tules and upriver brights. We sampled recreationally landed Chinook Salmon in 2017, 2018, and 2019, assigned sampled individuals to functional reporting groups using a single-nucleotide-polymorphism-based genetic baseline, and collected measurements on phenotypic traits. Using traits including pigmentation patterns (e.g., spotting), fin morphology, characteristics indicative of sexual maturity, and muscle lipid content, random forest classification models provided consistently high classification success across and within genetic groups (i.e., up to 90%). Classification success remained consistent over time within fishery seasons and between years but showed meaningful bias between sexes. Based on observed classification success, we developed and evaluated a categorical visual identification guide capable of facilitating more rapid trait observations and on-site stock identification. The resulting classification key, built using classification trees and visual guide observations from 2019, achieved slightly lower classification success across and within genetic groups and had variable success among samplers. Compared with the existing use of coded wire tags in harvest assessment, phenotypic stock identification methods can provide more rapid and more numerous assignments, albeit with a greater degree of individual assignment error. Applied as a complement to standard methods like coded wire tags, the use of rapid phenotypic stock identification methods offers the potential for increased overall precision and timeliness in harvest assessments.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/mcf2.10145","usgsCitation":"Jensen, A.J., Schreck, C., and Peterson, J., 2021, Rapid phenotypic stock identification of Chinook Salmon in recreational fishery management: Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science, v. 13, no. 2, p. 99-112, https://doi.org/10.1002/mcf2.10145.","productDescription":"14 p.","startPage":"99","endPage":"112","ipdsId":"IP-120670","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":452917,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/mcf2.10145","text":"Publisher Index Page"},{"id":396565,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon, Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.19219970703125,\n 46.07894655768008\n ],\n [\n -123.42041015624999,\n 46.07894655768008\n ],\n [\n -123.42041015624999,\n 46.4056700993737\n ],\n [\n -124.19219970703125,\n 46.4056700993737\n ],\n [\n -124.19219970703125,\n 46.07894655768008\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-03-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Jensen, Alexander J.","contributorId":286918,"corporation":false,"usgs":false,"family":"Jensen","given":"Alexander","email":"","middleInitial":"J.","affiliations":[{"id":25426,"text":"OSU","active":true,"usgs":false}],"preferred":false,"id":836360,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schreck, Carl B.","contributorId":286917,"corporation":false,"usgs":false,"family":"Schreck","given":"Carl B.","affiliations":[{"id":25426,"text":"OSU","active":true,"usgs":false}],"preferred":false,"id":836359,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peterson, James T. 0000-0002-7709-8590 james_peterson@usgs.gov","orcid":"https://orcid.org/0000-0002-7709-8590","contributorId":2111,"corporation":false,"usgs":true,"family":"Peterson","given":"James","email":"james_peterson@usgs.gov","middleInitial":"T.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":836358,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70220217,"text":"70220217 - 2021 - Implications of model selection: A comparison of publicly available, conterminous US-extent hydrologic component estimates","interactions":[],"lastModifiedDate":"2021-04-29T11:57:20.595886","indexId":"70220217","displayToPublicDate":"2021-03-26T08:20:57","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1928,"text":"Hydrology and Earth System Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Implications of model selection: A comparison of publicly available, conterminous US-extent hydrologic component estimates","docAbstract":"Spatiotemporally continuous estimates of the hydrologic cycle are often generated through hydrologic modeling, reanalysis, or remote sensing (RS) methods and are commonly applied as a supplement to, or a substitute for, in situ measurements when observational data are sparse or unavailable. This study compares estimates of precipitation (P), actual evapotranspiration (ET), runoff (R), snow water equivalent (SWE), and soil moisture (SM) from 87 unique data sets generated by 47 hydrologic models, reanalysis data sets, and remote sensing products across the conterminous United States (CONUS). Uncertainty between hydrologic component estimates was shown to be high in the western CONUS, with median uncertainty (measured as the coefficient of variation) ranging from 11 % to 21 % for P, 14 % to 26 % for ET, 28 % to 82 % for R, 76 % to 84 % for SWE, and 36 % to 96 % for SM. Uncertainty between estimates was lower in the eastern CONUS, with medians ranging from 5 % to 14 % for P, 13 % to 22 % for ET, 28 % to 82 % for R, 53 % to 63 % for SWE, and 42 % to 83 % for SM. Interannual trends in estimates from 1982 to 2010 show common disagreement in R, SWE, and SM. Correlating fluxes and stores against remote-sensing-derived products show poor overall correlation in the western CONUS for ET and SM estimates. Study results show that disagreement between estimates can be substantial, sometimes exceeding the magnitude of the measurements themselves. The authors conclude that multimodel ensembles are not only useful but are in fact a necessity for accurately representing uncertainty in research results. Spatial biases of model disagreement values in the western United States show that targeted research efforts in arid and semiarid water-limited regions are warranted, with the greatest emphasis on storage and runoff components, to better describe complexities of the terrestrial hydrologic system and reconcile model disagreement.
","language":"English","publisher":"Copernicus","doi":"10.5194/hess-25-1529-2021","usgsCitation":"Saxe, S., Farmer, W., Driscoll, J.M., and Hogue, T.S., 2021, Implications of model selection: A comparison of publicly available, conterminous US-extent hydrologic component estimates: Hydrology and Earth System Sciences, v. 25, p. 1529-1598, https://doi.org/10.5194/hess-25-1529-2021.","productDescription":"70 p.","startPage":"1529","endPage":"1598","ipdsId":"IP-117307","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":452922,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/hess-25-1529-2021","text":"Publisher Index Page"},{"id":436432,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9588YM2","text":"USGS data release","linkHelpText":"Collection of Hydrologic Models, Reanalysis Datasets, and Remote Sensing Products Aggregated by 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0000-0003-1151-8908","orcid":"https://orcid.org/0000-0003-1151-8908","contributorId":215753,"corporation":false,"usgs":true,"family":"Saxe","given":"Samuel","email":"","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":814837,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Farmer, William H. 0000-0002-2865-2196","orcid":"https://orcid.org/0000-0002-2865-2196","contributorId":223181,"corporation":false,"usgs":true,"family":"Farmer","given":"William H.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":814838,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Driscoll, Jessica M. 0000-0003-3097-9603 jdriscoll@usgs.gov","orcid":"https://orcid.org/0000-0003-3097-9603","contributorId":167585,"corporation":false,"usgs":true,"family":"Driscoll","given":"Jessica","email":"jdriscoll@usgs.gov","middleInitial":"M.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":814839,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hogue, Terri S.","contributorId":205175,"corporation":false,"usgs":false,"family":"Hogue","given":"Terri","email":"","middleInitial":"S.","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":814840,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70219422,"text":"70219422 - 2021 - Mammal species composition and habitat associations in a commercial forest and mixed-plantation landscape","interactions":[],"lastModifiedDate":"2021-04-05T13:10:25.140287","indexId":"70219422","displayToPublicDate":"2021-03-26T08:08:51","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1687,"text":"Forest Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Mammal species composition and habitat associations in a commercial forest and mixed-plantation landscape","docAbstract":"Commercial forest plantations of fast-growing species have been established globally to meet increasing demands for timber, pulpwood, and other wood products. Industrial plantations may contribute to tropical forest conservation by reducing exploitation of primary and secondary natural forests. Whether such plantations can support critical elements of biodiversity, including provision of habitat and movement corridors for species of conservation concern, is an important question in Southeast Asia. Our objectives were to investigate relationships between habitat gradients and community attributes of medium-sized to large mammals in a mixed plantation mosaic in Bengkoka Peninsula, Sabah, East Malaysia. Data on mammals were collected using 59 remote camera stations deployed for a minimum of 21 days (24-hour sampling occasions) in three major land-use types: natural forest, Acacia plantations, and non-Acacia plantations (oil palm, rubber, young Eucalyptus pellita). We used sample-based rarefaction to evaluate variation in species richness with land use. We used generalized linear models and ordination analyses to evaluate whether variation in mammal detections and species composition was associated with habitat gradients. We recorded >22 mammal species over 1572 sampling occasions. Natural forest area was positively associated with mammal species richness and detections of threatened mammals. Overall detections of mammals increased with decreasing elevation, but decreased within, and close to, Acacia plantations. Detections of threatened mammals increased with greater proportions of natural forest and Acacia and increasing proximity to roads. Sample-based rarefaction indicated that species richness of mammals in Acacia and natural forest was considerably higher than observed. Both natural forest and Acacia plantations shared similar values for species richness and diversity, but non-Acacia plantations scored lower in both metrics. Mammal species composition differed among different types of land use. Smaller generalists used non-Acacia plantation forests. A variety of other mammals including some threatened species used natural forest, Acacia, or a combination of the two. Acacia plantations possess attributes supporting a diversity of mammal species, including those we defined as threatened based on IUCN criteria. However, this is likely a function of the habitat mosaic with natural forest in the study area and the mangrove forests on the fringes of the peninsula serving as refuges of mammal diversity. Retention and restoration of natural and mangrove forests may therefore enhance the conservation potential of industrial Acacia plantations. Additionally, controlled road access in conjunction with anti-poaching operations and strengthening public awareness are essential to reduce the threat of overexploitation.
The unsaturated zone (UZ) extends across the Earth’s terrestrial surface and is central to many problems related to land and water resource management. Flow of water through the UZ is typically thought to be slow and diffusive, such that it could attenuate fluxes and dampen variability between atmospheric inputs and underlying aquifer systems. This would reduce water resource vulnerability to contaminants and water-related hazards. Reducing or negating that effect, however, spatially concentrated and rapid flow and transport through the unsaturated zone is surprisingly common and becoming more so with the increasing frequency and magnitude of extreme hydroclimatic events. Arising from the wide range in the rates and complex modes of nonlinear flow processes, these effects are among the most poorly characterized hydrologic phenomena. Issues of scale present additional difficulties. Equations representing unsaturated processes have been developed and tested on the basis of field and laboratory measurements typically made at scales from pore size to plot size. In contrast, related problems of significant interest to society, including floods, aquifer recharge, landslides, and groundwater contamination, range from watershed to regional scales. The disparity between the scale of our understanding and the scale of interest for societal problems has spurred application of these model equations at increasingly coarse resolutions over larger areas than can be justified by existing measurements or theory. This mismatch in scales requires an assumption that spatially averaging slow diffusive flow and rapid preferential flow can effectively represent the influence of both processes across vast areas. Given the currently inadequate recognition and quantitative characterization of focused and rapid processes in unsaturated flow, these phenomena are critically in need of expanded attention and effort.
A key assumption in species distribution modeling (SDM) with presence‐background (PB) methods is that sampling of occurrence localities is unbiased and that any sampling bias is proportional to the background distribution of environmental covariates. This assumption is rarely met when SDM practitioners rely on federated museum records from natural history collections for geo‐located occurrences due to inherent sampling bias found in these collections. We use a simulation approach to explore the effectiveness of three methods developed to account for sampling bias in SDM with PB frameworks. Two of the methods rely on careful filtering of observation data—geographic thinning (G‐Filter) and environmental thinning (E‐Filter)—while a third, FactorBiasOut, creates selection weights for background data to bias locations toward areas where the observation dataset was sampled. While these methods have been assessed previously, evaluation has emphasized spatial predictions of habitat potential. Here, we dig deeper into the effectiveness of these methods by exploring how sampling bias not only affects predictions of habitat potential, but also our understanding of niche characteristics such as which explanatory variables and response curves best represent species–environment relationships. We simulate 100 virtual species ranging from generalist to specialist in their habitat preferences and introduce geographic and environmental bias at three intensity levels to measure the effectiveness of each correction method to (1) predict true probability of occurrence across a study area, (2) recover true species–environment relationships, and (3) identify true explanatory variables. We find that the FactorBiasOut most often showed the greatest improvement in recreating known distributions but did no better at correctly identifying environmental covariates or recreating species–environment relationships than G‐Filter or E‐Filter methods. Narrow niche species are most problematic for biased calibration datasets, such that correction methods can, in some cases, make predictions worse.
Despite the importance of species–area relationships (SARs) to conservation, SARs in human‐fragmented rivers have received little attention. Our aim was to test for the presence and strength of SARs for littoral fish assemblages of an extensively dammed river in south‐central Ontario, Canada, and to examine long‐running hypotheses for the drivers of SARs. Twenty‐six navigational dams with locks built between 1837 and 1913 occur along the 160 km length of the Trent River examined in this study. We evaluated the relationship between richness and fragment area, and then used linear models to test whether the area per se, habitat diversity, or other hypotheses were best supported by the data. A power–function relationship with area explained 46% of the variation in fish species richness, and the slope (z = 0.4) was high compared with SARs reported from other ecosystems, indicating that species accumulated rapidly with an increase in fragment area. Multi‐predictor models suggested that area was significantly related to richness, but that vegetation cover diversity had a stronger relative effect. The slope of our SAR may indicate that there is a high degree of isolation between populations in different fragments, even though the lock system reportedly allows some passage of organisms. Our findings also suggest that mitigating against local extinction due to small population sizes (i.e., area effects), and enhancing aquatic vegetation cover may be viable strategies for promoting species diversity in the study river. Studies of SARs in fragmented rivers may offer additional benefits to supporting restoration planning where efforts are being made to increase species diversity.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.3411","usgsCitation":"Carl, L.M., Esselman, P., Sparks-Jackson, B.L., and Wilson, C.C., 2021, The species–area relationship for a highly fragmented temperate river system: Ecosphere, v. 12, no. 3, e03411, 17 p., https://doi.org/10.1002/ecs2.3411.","productDescription":"e03411, 17 p.","ipdsId":"IP-074620","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":490069,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3411","text":"Publisher Index Page"},{"id":436433,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9L1AYLW","text":"USGS data release","linkHelpText":"Habitat and fish assemblages along four river mainstems in Ontario, Canada, 1997 to 2001, with supporting spatial data"},{"id":384698,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada","state":"Ontario","otherGeospatial":"Rice Lake, Trent River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.99468994140625,\n 44.43476045009948\n ],\n [\n -78.41629028320312,\n 44.173339873464684\n ],\n [\n -78.28582763671875,\n 43.94537239244209\n ],\n [\n -78.01803588867188,\n 43.95822503841972\n ],\n [\n -77.86972045898438,\n 43.982933852960805\n ],\n [\n -77.73101806640625,\n 43.982933852960805\n ],\n [\n -77.57858276367188,\n 44.049102784014536\n ],\n [\n -77.574462890625,\n 44.26683800273895\n ],\n [\n -77.99468994140625,\n 44.43476045009948\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-03-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Carl, Leon M. 0000-0001-6419-2214 lcarl@usgs.gov","orcid":"https://orcid.org/0000-0001-6419-2214","contributorId":256693,"corporation":false,"usgs":true,"family":"Carl","given":"Leon","email":"lcarl@usgs.gov","middleInitial":"M.","affiliations":[{"id":5068,"text":"Midwest Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":813065,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Esselman, Peter C. 0000-0002-0085-903X","orcid":"https://orcid.org/0000-0002-0085-903X","contributorId":204291,"corporation":false,"usgs":true,"family":"Esselman","given":"Peter C.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":813066,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sparks-Jackson, Beth L 0000-0002-1726-1480","orcid":"https://orcid.org/0000-0002-1726-1480","contributorId":256695,"corporation":false,"usgs":false,"family":"Sparks-Jackson","given":"Beth","email":"","middleInitial":"L","affiliations":[{"id":51831,"text":"Contractor to USGS Great Lakes Science Center","active":true,"usgs":false}],"preferred":false,"id":813067,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wilson, Christopher C. 0000-0002-9528-0652","orcid":"https://orcid.org/0000-0002-9528-0652","contributorId":256696,"corporation":false,"usgs":false,"family":"Wilson","given":"Christopher","email":"","middleInitial":"C.","affiliations":[{"id":51832,"text":"Aquatic Biodiversity and Conservation Unit, Ontario Ministry of Natural Resources, Peterborough, ON, Canada","active":true,"usgs":false}],"preferred":false,"id":813068,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70219125,"text":"sir20205140 - 2021 - Evaluation and application of the Purge Analyzer Tool (PAT) to determine in-well flow and purge criteria for sampling monitoring wells at the Stringfellow Superfund site in Jurupa Valley, California, in 2017","interactions":[],"lastModifiedDate":"2021-03-25T15:53:32.129298","indexId":"sir20205140","displayToPublicDate":"2021-03-25T09:45:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5140","displayTitle":"Evaluation and Application of the Purge Analyzer Tool (PAT) To Determine In-Well Flow and Purge Criteria for Sampling Monitoring Wells at the Stringfellow Superfund Site in Jurupa Valley, California, in 2017","title":"Evaluation and application of the Purge Analyzer Tool (PAT) to determine in-well flow and purge criteria for sampling monitoring wells at the Stringfellow Superfund site in Jurupa Valley, California, in 2017","docAbstract":"The U.S. Geological Survey and U.S. Environmental Protection Agency are developing analytical tools to assess the representativeness of groundwater samples from fractured-rock aquifers. As part of this effort, monitoring wells from the Stringfellow Superfund site in Jurupa Valley in Riverside County, California, approximately 50 miles east of Los Angeles, were field tested to collect information to assist in the evaluation and application of in-well flow as computed by the analytical model called the Purge Analyzer Tool, which computes in-well groundwater travel times for simple piston transport of inflowing groundwater from open intervals of a monitoring well to the pump intake and can provide insight into optimal purging parameters (duration, rate, and pump position) needed for the collection of representative groundwater samples. Field testing of wells included hydraulic, chemistry, and dye tracer analysis to investigate travel times in wells under pumping conditions. The Purge Analyzer Tool was able to replicate dye velocities (travel times) for one of three wells that had appreciable inflow from the aquifer but not the other two wells, which are screened in low-permeability sediments and rock, where flow was dominated by borehole storage. A set of criteria was established to help assess the ability to collect representative groundwater chemistry from monitoring wells; criteria included understanding the height of the static well water column and relative exchange rate between the aquifer and the well.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205140","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Harte, P.T., Perina, T., Becher, K., Levine, H., Rojas-Mickelson, D., Walther, L., and Brown, A., 2021, Evaluation and application of the Purge Analyzer Tool (PAT) to determine in-well flow and purge criteria for sampling monitoring wells at the Stringfellow Superfund site in Jurupa Valley, California, in 2017: U.S. Geological Survey Scientific Investigations Report 2020–5140, 54 p., https://doi.org/10.3133/sir20205140.","productDescription":"Report: ix, 54 p.; Data Release","numberOfPages":"54","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-103064","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":384640,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20191104","text":"Open-File Report 2019–1104","linkHelpText":"- Instructions for Running the Analytical Code PAT (Purge Analyzer Tool) for Computation of In-Well Time of Travel of Groundwater under Pumping Conditions"},{"id":384639,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CGINH0","text":"USGS data release","linkHelpText":"Data associated with the evaluation of the PAT (Purge Analyzer Tool), Stringfellow Superfund site, Jurupa Valley, California, 2017"},{"id":384641,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5140/coverthb.jpg"},{"id":384642,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5140/sir20205140.pdf","text":"Report","size":"5.07 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5140"}],"country":"United States","state":"California","city":"Jurupa Valley","otherGeospatial":"Stringfellow Superfund Site","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.47066974639893,\n 34.019585556900935\n ],\n [\n -117.45178699493408,\n 34.019585556900935\n ],\n [\n -117.45178699493408,\n 34.03078943899101\n ],\n [\n -117.47066974639893,\n 34.03078943899101\n ],\n [\n -117.47066974639893,\n 34.019585556900935\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Slow-moving landslides are destabilized by accumulated precipitation and consequent soil moisture. Yet, the continuous high-resolution soil-moisture measurements needed to aid the understanding of landslide processes are generally absent in steep terrain. Here, we produce soil-moisture time-series maps for a seasonally active grassland landslide in the northern California coast ranges, USA, using backscattering coefficients from NASA's uninhabited aerial vehicle synthetic aperture radar at 6-m resolution. A physically based radar scattering model is used to retrieve the near-surface (5-cm depth) soil moisture for the landslide. Both forward modeling (backscattering estimation) and the retrieval (soil-moisture validation) show good agreement. The root-mean-square errors (RMSE) for vertical transmit vertical receive (VV) and horizontal transmit horizontal receive (HH) polarizations in forward model comparison are 1.93 dB and 1.88 dB, respectively. The soil-moisture retrieval shows unbiased RMSE of 0.054 m 3 /m 3 . Our successful retrieval benefits from the surface and double-bounce scattering, which is common in grasslands. The retrieved maps show saturated wetness conditions within the active landslide boundaries. We also performed sensitivity tests for incidence angle and found that the retrieval is weakly dependent on the angle, especially while using copolarized HH and VV together. Using the two copolarized inputs, the retrieval is also not sensitive to the change of orientation angles of grass cylinders. The physical model inversion presented here can be generally applied for soil-moisture retrieval in areas with the same vegetation cover types in California.
","language":"English","publisher":"IEEE","doi":"10.1109/JSTARS.2021.3069010","usgsCitation":"Liao, T., Kim, S., Handwerger, A.L., Fielding, E.J., Cosh, M.H., and Schulz, W.H., 2021, High-resolution soil-moisture maps over landslide regions in northern California grassland derived From SAR backscattering coefficients: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, v. 14, p. 4547-4560, https://doi.org/10.1109/JSTARS.2021.3069010.","productDescription":"14 p.","startPage":"4547","endPage":"4560","ipdsId":"IP-126334","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":452942,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1109/jstars.2021.3069010","text":"Publisher Index Page"},{"id":436434,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NSUMOY","text":"USGS data release","linkHelpText":"Precipitation and soil-moisture data from the Two Towers landslide, Trinity County, California"},{"id":394381,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Eel River catchment, Two Towers landslide","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.46761703491211,\n 40.09832911511634\n ],\n [\n -123.45680236816408,\n 40.09832911511634\n ],\n [\n -123.45680236816408,\n 40.10669965638878\n ],\n [\n -123.46761703491211,\n 40.10669965638878\n ],\n [\n -123.46761703491211,\n 40.09832911511634\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"14","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Liao, Tien-Hao","contributorId":271098,"corporation":false,"usgs":false,"family":"Liao","given":"Tien-Hao","email":"","affiliations":[{"id":56278,"text":"California Institute of Technology, Division Office Geological and Planetary Sciences","active":true,"usgs":false}],"preferred":false,"id":830812,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kim, Seung-bum","contributorId":244954,"corporation":false,"usgs":false,"family":"Kim","given":"Seung-bum","email":"","affiliations":[{"id":27365,"text":"NASA Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":830813,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Handwerger, Alexander L.","contributorId":218095,"corporation":false,"usgs":false,"family":"Handwerger","given":"Alexander","email":"","middleInitial":"L.","affiliations":[{"id":39742,"text":"Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA.","active":true,"usgs":false}],"preferred":false,"id":830814,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fielding, Eric J.","contributorId":218096,"corporation":false,"usgs":false,"family":"Fielding","given":"Eric","email":"","middleInitial":"J.","affiliations":[{"id":39742,"text":"Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA.","active":true,"usgs":false}],"preferred":false,"id":830815,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cosh, Michael H.","contributorId":146998,"corporation":false,"usgs":false,"family":"Cosh","given":"Michael","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":830816,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schulz, William H. 0000-0001-9980-3580 wschulz@usgs.gov","orcid":"https://orcid.org/0000-0001-9980-3580","contributorId":942,"corporation":false,"usgs":true,"family":"Schulz","given":"William","email":"wschulz@usgs.gov","middleInitial":"H.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":830817,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ]}