In this chapter we describe the software we have developed for photogrammetric processing of images from the Mars Express High Resolution Stereo Camera (MEX HRSC) to produce digital topographic models (DTMs) and orthoimages, as well as testing we have performed. HRSC has returned images, including stereo and color coverage of most of Mars at decameter scales. The instrument team has developed an extremely powerful processing pipeline and delivered a large number of high-level data products, but our independent software is nevertheless of interest because it provides a check on the standard products, sheds light on the capabilities of software elements we use for multiple missions besides HRSC, and is publicly available, giving users the opportunity to make products that may not (yet) be released by the team and custom products such as local mosaics. We have tested our software on images of three areas: Candor Chasma and Nanedi Valles (both the subject of past DTM comparisons reported by Heipke et al., 2007) and Gale crater, which was extensively mapped at pixel scales 50 times finer than HRSC before its selection as the landing site of the Curiosity rover. We find the vertical precision and mean deviation from the altimetry data used as a control reference for our DTMs to be comparable to the nadir image pixel size. The horizontal resolution of the DTMs appears to be an order of magnitude coarser than the lower limit of 3–5 image pixels that is commonly stated.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Planetary remote sensing and mapping","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Taylor & Francis","doi":"10.1201/9780429505997-9","usgsCitation":"Kirk, R.L., Howington-Kraus, E., Edmundson, K., Redding, B.L., Galuszka, D.M., Hare, T.M., and Gwinner, K., 2020, Community tools for cartographic and photogrammetric processing of Mars Express HRSC images, chap. 9 of Planetary remote sensing and mapping, p. 107-124, https://doi.org/10.1201/9780429505997-9.","productDescription":"18 p.","startPage":"107","endPage":"124","ipdsId":"IP-095329","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":377830,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Mars","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kirk, Randolph L. 0000-0003-0842-9226 rkirk@usgs.gov","orcid":"https://orcid.org/0000-0003-0842-9226","contributorId":2765,"corporation":false,"usgs":true,"family":"Kirk","given":"Randolph","email":"rkirk@usgs.gov","middleInitial":"L.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":797230,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Howington-Kraus, Elpitha 0000-0001-5787-6554 ahowington@usgs.gov","orcid":"https://orcid.org/0000-0001-5787-6554","contributorId":2815,"corporation":false,"usgs":true,"family":"Howington-Kraus","given":"Elpitha","email":"ahowington@usgs.gov","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":797231,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Edmundson, Kenneth 0000-0003-3666-0927 kedmundson@usgs.gov","orcid":"https://orcid.org/0000-0003-3666-0927","contributorId":206340,"corporation":false,"usgs":true,"family":"Edmundson","given":"Kenneth","email":"kedmundson@usgs.gov","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":797232,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Redding, Bonnie L. 0000-0001-8178-1467 bredding@usgs.gov","orcid":"https://orcid.org/0000-0001-8178-1467","contributorId":4798,"corporation":false,"usgs":true,"family":"Redding","given":"Bonnie","email":"bredding@usgs.gov","middleInitial":"L.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":797233,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Galuszka, Donna M. 0000-0003-1870-1182 dgaluszka@usgs.gov","orcid":"https://orcid.org/0000-0003-1870-1182","contributorId":3186,"corporation":false,"usgs":true,"family":"Galuszka","given":"Donna","email":"dgaluszka@usgs.gov","middleInitial":"M.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":797234,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hare, Trent M. 0000-0001-8842-389X thare@usgs.gov","orcid":"https://orcid.org/0000-0001-8842-389X","contributorId":3188,"corporation":false,"usgs":true,"family":"Hare","given":"Trent","email":"thare@usgs.gov","middleInitial":"M.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":797235,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gwinner, K.","contributorId":239565,"corporation":false,"usgs":false,"family":"Gwinner","given":"K.","affiliations":[{"id":47920,"text":"German Aerospace Center DLR","active":true,"usgs":false}],"preferred":false,"id":797236,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70213188,"text":"70213188 - 2020 - Observations and recommendations for coordinated calibration activities of government and commercial optical satellite systems","interactions":[],"lastModifiedDate":"2021-04-01T16:52:44.024795","indexId":"70213188","displayToPublicDate":"2018-08-22T08:56:55","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Observations and recommendations for coordinated calibration activities of government and commercial optical satellite systems","docAbstract":"One of the biggest changes in the world of optical remote sensing over the last several years is the sheer increase in the number of sensors that are imaging the Earth in moderate to high spatial resolution. With respect to the calibration of these sensors, they are broadly classified into two types, namely government systems and commercial systems. Because of the differences in the design and mission of these sensor types, calibration approaches are often substantially different. Thus, an opportunity exists to foster discussion between calibration teams for these sensors with the goal of improving overall sensor calibration and data interoperability. The approach used to accomplish this task was a one-day workshop where team members from both government and commercial sensors could share best practices, discuss methods for collaboration and improvement, and make recommendations for continuing activities. Five major recommendations were developed from the event that focused on coordinated activities using pseudo invariant calibration sites (PICS), broader and more consistent communication, collaboration on specific cross-calibration opportunities, developing a reference sensor for all optical systems, and encouraging the coordinated development of surface reflectance products. Workshop participants concluded that regular interactions between these teams could foster a better calibration of all sensor systems and accelerate the improved interoperability of surface products.
","language":"English","publisher":"MDPI","doi":"10.3390/rs12152468","usgsCitation":"Helder, D., Anderson, C., Beckett, K., Houborg, R., Zuleta, I., Boccia, V., Clerc, S., Kuester, M., Brian Markham, and Pagnutti, M., 2020, Observations and recommendations for coordinated calibration activities of government and commercial optical satellite systems: Remote Sensing, v. 12, no. 15, 2468, 17 p., https://doi.org/10.3390/rs12152468.","productDescription":"2468, 17 p.","ipdsId":"IP-117839","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":458797,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs12152468","text":"Publisher Index Page"},{"id":378354,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"15","noUsgsAuthors":false,"publicationDate":"2020-07-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Helder, Dennis 0000-0002-7379-4679","orcid":"https://orcid.org/0000-0002-7379-4679","contributorId":213606,"corporation":false,"usgs":true,"family":"Helder","given":"Dennis","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":798548,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Cody 0000-0001-5612-1889 chanderson@usgs.gov","orcid":"https://orcid.org/0000-0001-5612-1889","contributorId":195521,"corporation":false,"usgs":true,"family":"Anderson","given":"Cody","email":"chanderson@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":813422,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beckett, Keith","contributorId":240605,"corporation":false,"usgs":false,"family":"Beckett","given":"Keith","email":"","affiliations":[{"id":48112,"text":"Planet","active":true,"usgs":false}],"preferred":false,"id":813423,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Houborg, Rasmus","contributorId":240608,"corporation":false,"usgs":false,"family":"Houborg","given":"Rasmus","email":"","affiliations":[{"id":48112,"text":"Planet","active":true,"usgs":false}],"preferred":false,"id":813424,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zuleta, Ignacio","contributorId":240611,"corporation":false,"usgs":false,"family":"Zuleta","given":"Ignacio","email":"","affiliations":[{"id":48112,"text":"Planet","active":true,"usgs":false}],"preferred":false,"id":813425,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Boccia, Valentina","contributorId":240606,"corporation":false,"usgs":false,"family":"Boccia","given":"Valentina","email":"","affiliations":[{"id":38836,"text":"European Space Agency","active":true,"usgs":false}],"preferred":false,"id":813426,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Clerc, Sebastian","contributorId":240607,"corporation":false,"usgs":false,"family":"Clerc","given":"Sebastian","email":"","affiliations":[{"id":48113,"text":"ACRI-ST","active":true,"usgs":false}],"preferred":false,"id":798550,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kuester, Michele","contributorId":240609,"corporation":false,"usgs":false,"family":"Kuester","given":"Michele","email":"","affiliations":[{"id":48114,"text":"Maxar","active":true,"usgs":false}],"preferred":false,"id":813427,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Brian Markham","contributorId":241117,"corporation":false,"usgs":false,"family":"Brian Markham","affiliations":[{"id":39055,"text":"NASA GSFC","active":true,"usgs":false}],"preferred":false,"id":813428,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Pagnutti, M.","contributorId":69874,"corporation":false,"usgs":true,"family":"Pagnutti","given":"M.","affiliations":[],"preferred":false,"id":813429,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70208704,"text":"70208704 - 2020 - When portfolio theory can help environmental investment planning to reduce climate risk to future environmental outcomes - and when it cannot","interactions":[],"lastModifiedDate":"2020-02-26T06:16:43","indexId":"70208704","displayToPublicDate":"2018-07-12T12:28:59","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1326,"text":"Conservation Letters","active":true,"publicationSubtype":{"id":10}},"title":"When portfolio theory can help environmental investment planning to reduce climate risk to future environmental outcomes - and when it cannot","docAbstract":"Variability among climate change scenarios produces great uncertainty in what is the best allocation of resources among investments to protect environmental goods in the future. Previous research shows Modern Portfolio Theory (MPT) can help optimize environmental investment targeting to reduce outcome risk with minimal loss of expected level of environmental benefits, but no work has yet identified the types of cases for which MPT is most useful. This paper assembles data on 26 different conservation cases in three distinct ecological settings and develops new metrics to evaluate how well MPT can reduce uncertainty in future outcomes of a set of environmental investments. We find MPT is broadly but not universally useful and works best when multiple investments have negatively correlated outcomes across climate scenarios, a second-best investment has expected value almost as good as the value in the best investment; or multiple investments have little uncertainty in ecological outcomes.","language":"English","publisher":"Wiley","doi":"10.1111/conl.12596","usgsCitation":"Ando, A.W., Fraterrigo, J.M., Guntenspergen, G.R., Howlader, A., Mallory, M.L., Olker, J.H., and Stickley, S., 2020, When portfolio theory can help environmental investment planning to reduce climate risk to future environmental outcomes - and when it cannot: Conservation Letters, v. 11, no. 6, e12596, 10 p., https://doi.org/10.1111/conl.12596.","productDescription":"e12596, 10 p.","ipdsId":"IP-098951","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":458799,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/conl.12596","text":"Publisher Index Page"},{"id":372628,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"6","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2018-07-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Ando, Amy W.","contributorId":189611,"corporation":false,"usgs":false,"family":"Ando","given":"Amy","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":783117,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fraterrigo, Jennifer M.","contributorId":150046,"corporation":false,"usgs":false,"family":"Fraterrigo","given":"Jennifer","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":783118,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Guntenspergen, Glenn R. 0000-0002-8593-0244 glenn_guntenspergen@usgs.gov","orcid":"https://orcid.org/0000-0002-8593-0244","contributorId":2885,"corporation":false,"usgs":true,"family":"Guntenspergen","given":"Glenn","email":"glenn_guntenspergen@usgs.gov","middleInitial":"R.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":783119,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Howlader, Aparna","contributorId":222772,"corporation":false,"usgs":false,"family":"Howlader","given":"Aparna","email":"","affiliations":[],"preferred":false,"id":783120,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mallory, Mindy L.","contributorId":189610,"corporation":false,"usgs":false,"family":"Mallory","given":"Mindy","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":783121,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Olker, Jennifer H.","contributorId":208040,"corporation":false,"usgs":false,"family":"Olker","given":"Jennifer","email":"","middleInitial":"H.","affiliations":[{"id":6915,"text":"University of Minnesota - Duluth","active":true,"usgs":false}],"preferred":false,"id":783122,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Stickley, Samuel","contributorId":222773,"corporation":false,"usgs":false,"family":"Stickley","given":"Samuel","email":"","affiliations":[],"preferred":false,"id":783123,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70212046,"text":"70212046 - 2020 - Mapping climate change resistant vernal pools in the northeastern U.S.","interactions":[],"lastModifiedDate":"2021-09-22T15:37:46.324155","indexId":"70212046","displayToPublicDate":"2017-12-31T10:37:14","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5883,"text":"Cooperator Report","active":true,"publicationSubtype":{"id":1}},"title":"Mapping climate change resistant vernal pools in the northeastern U.S.","docAbstract":"Vernal pools are seasonal wetlands that provide important breeding habitat for a variety of amphibian species. As future climate projections indicate warmer growing seasons and earlier seasonal increases in evapotranspiration, some managers of vernal pools have expressed concern that pools may dry earlier in the season, potentially interfering with completion of amphibian life cycles. In this context, a subset of pools might function as hydrologic refugia by providing wetland habitat later into the year under relatively dry conditions, thus supporting species persistence even as summer conditions become warmer and droughts more frequent. This study used approximately 3,000 field observations of inundation from 450 pools in the northeastern United States—located from West Virginia to Maine—to train machine-learning models for predicting the likelihood of pool inundation. Inputs to these models included pool size, day of the year, climate conditions, short-term weather patterns, and attributes of the landscapes in which pools were embedded. Predictions of pool wetness were generated on a daily time step from late April through late July using three short-term weather scenarios (dry, wet, and average) under historical climate conditions and four sets of downscaled climate projections (2050s and 2080s under Representative Concentration Pathways 4.5 and 8.5). The modeling and inundation prediction process was replicated using four inundation thresholds on wetted area and depth. Model outputs can enable users to examine the inundation thresholds, time points, weather scenarios, and future climate projections most relevant to their management needs. Together with long-term monitoring of individual pools at the site scale, this regional-scale study can support amphibian conservation by helping to identify subsets of pools that may be most likely to function as hydrologic refugia from changing climate conditions.","language":"English","publisher":"Northeast Climate Adaptation Science Center","usgsCitation":"Cartwright, J.M., and Campbell Grant, E.H., 2020, Mapping climate change resistant vernal pools in the northeastern U.S.: Cooperator Report, HTML Document.","productDescription":"HTML Document","ipdsId":"IP-117745","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":389594,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":389593,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://necasc.umass.edu/projects/mapping-climate-change-resistant-vernal-pools-northeastern-us"}],"country":"United States","state":"Connecticut, Delaware, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Vermont, Virginia, West Virginia","otherGeospatial":"northeastern United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.025390625,\n 37.020098201368114\n ],\n [\n -66.97265625,\n 44.59046718130883\n ],\n [\n -68.64257812499999,\n 47.45780853075031\n ],\n [\n -80.947265625,\n 42.61779143282346\n ],\n [\n -81.38671875,\n 39.436192999314095\n ],\n [\n -76.025390625,\n 37.020098201368114\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Cartwright, Jennifer M. 0000-0003-0851-8456 jmcart@usgs.gov","orcid":"https://orcid.org/0000-0003-0851-8456","contributorId":5386,"corporation":false,"usgs":true,"family":"Cartwright","given":"Jennifer","email":"jmcart@usgs.gov","middleInitial":"M.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true}],"preferred":true,"id":796184,"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":796185,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70208360,"text":"70208360 - 2020 - Clawpack: Building an open source ecosystem for solving hyperbolic PDEs","interactions":[],"lastModifiedDate":"2020-02-05T15:48:30","indexId":"70208360","displayToPublicDate":"2016-08-08T15:45:30","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5926,"text":"PeerJ Computer Science","onlineIssn":"2376-5992","active":true,"publicationSubtype":{"id":10}},"title":"Clawpack: Building an open source ecosystem for solving hyperbolic PDEs","docAbstract":"Clawpack is a software package designed to solve nonlinear hyperbolic partial differential equations using high-resolution finite volume methods based on Riemann solvers and limiters. The package includes a number of variants aimed at different applications and user communities. Clawpack has been actively developed as an open source project for over 20 years. The latest major release, Clawpack 5, introduces a number of new features and changes to the code base and a new development model based on GitHub and Git submodules. This article provides a summary of the most significant changes, the rationale behind some of these changes, and a description of our current development model.","language":"English","publisher":"PeerJ, Inc.","doi":"10.7717/peerj-cs.68","usgsCitation":"Mandli, K.T., Ahmadia, A.J., Berger, M.J., Calhoun, D.A., George, D.L., Hadjimichael, Y., Ketcheson, D.I., Lemoine, G., and LeVeque, R.J., 2020, Clawpack: Building an open source ecosystem for solving hyperbolic PDEs: PeerJ Computer Science, v. 2, e68, 27 p., https://doi.org/10.7717/peerj-cs.68.","productDescription":"e68, 27 p.","ipdsId":"IP-076298","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":458806,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7717/peerj-cs.68","text":"Publisher Index Page"},{"id":372094,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-08-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Mandli, Kyle T.","contributorId":222227,"corporation":false,"usgs":false,"family":"Mandli","given":"Kyle","email":"","middleInitial":"T.","affiliations":[{"id":7171,"text":"Columbia University","active":true,"usgs":false}],"preferred":false,"id":781568,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ahmadia, Aron J.","contributorId":199787,"corporation":false,"usgs":false,"family":"Ahmadia","given":"Aron","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":781569,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Berger, Marsha J","contributorId":222228,"corporation":false,"usgs":false,"family":"Berger","given":"Marsha","email":"","middleInitial":"J","affiliations":[{"id":40508,"text":"New York University","active":true,"usgs":false}],"preferred":false,"id":781570,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Calhoun, Donna A","contributorId":222229,"corporation":false,"usgs":false,"family":"Calhoun","given":"Donna","email":"","middleInitial":"A","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":781571,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"George, David L. 0000-0002-5726-0255 dgeorge@usgs.gov","orcid":"https://orcid.org/0000-0002-5726-0255","contributorId":3120,"corporation":false,"usgs":true,"family":"George","given":"David","email":"dgeorge@usgs.gov","middleInitial":"L.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":781572,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hadjimichael, Yiannis","contributorId":222230,"corporation":false,"usgs":false,"family":"Hadjimichael","given":"Yiannis","email":"","affiliations":[{"id":35609,"text":"KAUST University","active":true,"usgs":false}],"preferred":false,"id":781573,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ketcheson, David I.","contributorId":199791,"corporation":false,"usgs":false,"family":"Ketcheson","given":"David","email":"","middleInitial":"I.","affiliations":[],"preferred":false,"id":781574,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lemoine, Grady I.","contributorId":222231,"corporation":false,"usgs":false,"family":"Lemoine","given":"Grady I.","affiliations":[{"id":35610,"text":"CD-Adapco","active":true,"usgs":false}],"preferred":false,"id":781575,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"LeVeque, Randall J.","contributorId":198359,"corporation":false,"usgs":false,"family":"LeVeque","given":"Randall","email":"","middleInitial":"J.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":781576,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70240329,"text":"70240329 - 2019 - Bighorn sheep habitat and model extrapolation across remote landscapes","interactions":[],"lastModifiedDate":"2023-02-06T15:05:41.116143","indexId":"70240329","displayToPublicDate":"2021-12-31T09:05:05","publicationYear":"2019","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Bighorn sheep habitat and model extrapolation across remote landscapes","docAbstract":"Determining a species’ habitat use is an essential first step in any wildlife conservation action. We described habitat use, animal movements and probable lambing areas in a remote, restricted-access region of the Mojave Desert. Differences in habitat use between sexes was apparent, supporting the often-reported concept of risk-aversion by females. Animals exhibited low variability in distances travelled, although males travelled further and with more variability than females. All females demonstrated what we interpret as lambing behavior during the same 2 ½ month periods over the two years, strongly supporting our inference of lambing sites. Water appeared critical to animal long-range movements, with no animal moving beyond 8.5 km from known sources. Modeling habitat use across the landscape of concern is another necessary step for conservation of species, allowing managers to plan for and predict the outcomes of management actions. In ecology, models are often created within relatively small areas, then extrapolated across larger regions of concern. The ability to extrapolate ecological models may be especially useful across remote areas, where consistent access by wildlife managers may be highly restricted. These restrictions on access require that most data be collected remotely, necessitating the need for extrapolating models developed in other areas. We used data from GPS-collared desert bighorn sheep to describe and model habitat use across the Pintwater Range, located on the Nevada Test and Training Range of southern Nevada, a highly restricted military training ground. We tested the efficacy of habitat model extrapolation by comparing the performance of two models derived from adjacent but independent desert bighorn sheep populations. The predictive power of seasonal habitat models derived from adjacent mountain ranges was lower than those derived from the local population. However, the performance of the extrapolated model suggests it could still be a feasible alternative for estimating general habitat use.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Desert Bighorn Council Transactions 2019: A compilation of papers presented at the 55th meeting","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Desert Bighorn Council Transactions 2019","conferenceDate":"April 17-19, 2019","conferenceLocation":"Mesquite, NV","language":"English","publisher":"Desert Bighorn Council","usgsCitation":"Lowrey, C., Schuster, S., Longshore, K., Cummings, P., Sprunger, A., Johnson, A., and Wilson-Henjum, G.E., 2019, Bighorn sheep habitat and model extrapolation across remote landscapes, in Desert Bighorn Council Transactions 2019: A compilation of papers presented at the 55th meeting, Mesquite, NV, April 17-19, 2019, p. 1-20.","productDescription":"20 p.","startPage":"1","endPage":"20","ipdsId":"IP-116209","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":412736,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":412735,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.desertbighorncouncil.com/transactions/download-past-dbc-transactions/"}],"country":"United States","state":"Nevada","county":"Clark County, Lincoln County, Nye County","otherGeospatial":"Nevada Test and Training Range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -115.96047652820107,\n 37.46829180279936\n ],\n [\n -115.96047652820107,\n 36.56268736637935\n ],\n [\n -115.28108193619912,\n 36.56268736637935\n ],\n [\n -115.28108193619912,\n 37.46829180279936\n ],\n [\n -115.96047652820107,\n 37.46829180279936\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lowrey, Chris 0000-0001-5084-7275","orcid":"https://orcid.org/0000-0001-5084-7275","contributorId":216375,"corporation":false,"usgs":true,"family":"Lowrey","given":"Chris","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":863426,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schuster, Sara","contributorId":302080,"corporation":false,"usgs":false,"family":"Schuster","given":"Sara","email":"","affiliations":[{"id":65407,"text":"Center for Environmental Management of Military Lands","active":true,"usgs":false}],"preferred":false,"id":863427,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Longshore, Kathleen 0000-0001-6621-1271","orcid":"https://orcid.org/0000-0001-6621-1271","contributorId":216374,"corporation":false,"usgs":true,"family":"Longshore","given":"Kathleen","email":"","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":863428,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cummings, Patrick","contributorId":174650,"corporation":false,"usgs":false,"family":"Cummings","given":"Patrick","email":"","affiliations":[{"id":27489,"text":"Nevada Department of Wildlife","active":true,"usgs":false}],"preferred":false,"id":863429,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sprunger, Amy","contributorId":302081,"corporation":false,"usgs":false,"family":"Sprunger","given":"Amy","email":"","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":863430,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Johnson, Anna","contributorId":287611,"corporation":false,"usgs":false,"family":"Johnson","given":"Anna","email":"","affiliations":[{"id":52650,"text":"Pennsylvania Natural Heritage Program","active":true,"usgs":false}],"preferred":false,"id":863431,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wilson-Henjum, Grete Elyse 0000-0002-2284-8745","orcid":"https://orcid.org/0000-0002-2284-8745","contributorId":302082,"corporation":false,"usgs":true,"family":"Wilson-Henjum","given":"Grete","email":"","middleInitial":"Elyse","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":863432,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70223407,"text":"70223407 - 2019 - Movement dynamics of smallmouth bass (Micropterus dolomieu) in a large river-tributary system","interactions":[],"lastModifiedDate":"2021-08-26T16:41:18.516743","indexId":"70223407","displayToPublicDate":"2021-08-26T09:55:59","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1659,"text":"Fisheries Management and Ecology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Movement dynamics of smallmouth bass (Micropterus dolomieu) in a large river-tributary system","title":"Movement dynamics of smallmouth bass (Micropterus dolomieu) in a large river-tributary system","docAbstract":"Smallmouth bass, Micropterus dolomieu Lacepède, movement dynamics were investigated in a connected mainstem river-tributary system. Smallmouth bass moved large distances annually (n = 84 fish, average = 24.6 ± 25.9 km, range = 0.03 to 118 km) and had three peak movement periods (pre-spawn, post-spawn and overwintering). Movement into and out of tributaries was common, but the movement between mainstem river and tributary habitats varied among tagging locations and season. In general, a large proportion of fish that were tagged in tributaries moved out of the tributaries after spawning (22/30 fish). Because of the importance of fish movement patterns on population dynamics, the observed individual variability in movement, quantified using a hierarchical model, and the potential for long-distance movements are important considerations for smallmouth bass conservation and management. In addition, mainstem river-tributary connectivity appears to play an important role for smallmouth bass during key life history events.
","language":"English","publisher":"Wiley","doi":"10.1111/fme.12369","usgsCitation":"Wagner, T., Schall, M., Timothy Wertz, Smith, G., and Blazer, V., 2019, Movement dynamics of smallmouth bass (Micropterus dolomieu) in a large river-tributary system: Fisheries Management and Ecology, v. 26, no. 6, p. 590-599, https://doi.org/10.1111/fme.12369.","productDescription":"10 p.","startPage":"590","endPage":"599","ipdsId":"IP-102962","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":388544,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","otherGeospatial":"West Branch Susquehanna River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.5302734375,\n 40.8034148344062\n ],\n [\n -76.190185546875,\n 40.8034148344062\n ],\n [\n -76.190185546875,\n 41.638025739250786\n ],\n [\n -78.5302734375,\n 41.638025739250786\n ],\n [\n -78.5302734375,\n 40.8034148344062\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"26","issue":"6","noUsgsAuthors":false,"publicationDate":"2019-06-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":821968,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schall, Megan K.","contributorId":264767,"corporation":false,"usgs":false,"family":"Schall","given":"Megan K.","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":821969,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Timothy Wertz","contributorId":264768,"corporation":false,"usgs":false,"family":"Timothy Wertz","affiliations":[{"id":54545,"text":"PA Department of Environmental Protection","active":true,"usgs":false}],"preferred":false,"id":821970,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, Geoffrey D.","contributorId":264769,"corporation":false,"usgs":false,"family":"Smith","given":"Geoffrey D.","affiliations":[{"id":54546,"text":"PA Fish and Boat Commission","active":true,"usgs":false}],"preferred":false,"id":821971,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Blazer, Vicki S. 0000-0001-6647-9614 vblazer@usgs.gov","orcid":"https://orcid.org/0000-0001-6647-9614","contributorId":150384,"corporation":false,"usgs":true,"family":"Blazer","given":"Vicki S.","email":"vblazer@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":821972,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70223194,"text":"70223194 - 2019 - Evaluation of an elk detection probability model in the Black Hills, South Dakota","interactions":[],"lastModifiedDate":"2021-08-17T12:36:09.959393","indexId":"70223194","displayToPublicDate":"2021-08-17T07:31:29","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":996,"text":"BioOne","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of an elk detection probability model in the Black Hills, South Dakota","docAbstract":"Since 1993, elk (Cervus canadensis nelsoni) abundance in the Black Hills of South Dakota has been estimated using a detection probability model previously developed in Idaho, though it is likely biased because of a failure to account for visibility biases under local conditions. To correct for this bias, we evaluated the current detection probability across the Black Hills during January and February 2009–2011 using radio-collared elk. We used logistic regression to evaluate topographic features, habitat characteristics, and group characteristics relative to their influence on detection probability of elk. Elk detection probability increased with less vegetation cover (%), increased group size, and more snow cover (%); overall detection probability was 0.60 (95% CI 0.52–0.68), with 91 of 152 elk groups detected. Predictive capability of the selected model was excellent (ROC = 0.807), and prediction accuracy ranged from 70.2% to 73.7%. Cross-validation of the selected model with other population estimation methods resulted in comparable estimates. Future applications of our model should be applied cautiously if characteristics of the area (e.g., vegetation cover >50%, snow cover >90%, group sizes >16 elk) differ notably from the range of variability in these factors under which the model was developed.
Director, Integrated Modeling and Prediction Division
Water Mission Area
U.S. Geological Survey
MS 415
12201 Sunrise Valley Drive
Reston, VA 20192
Volcanic plumes are complex environments composed of gases and ash particles, where chemical and physical processes occur at different temperature and compositional regimes. Commonly, soluble sulphate- and chloride-bearing salts are formed on ash as gases interact with ash surfaces. Exposure to respirable volcanic ash following an eruption is potentially a significant health concern. The impact of such gas-ash interactions on ash toxicity is wholly un-investigated. Here, we study, for the first time, whether the interaction of volcanic particles with sulphur dioxide (SO2) gas, and the resulting presence of sulphate salt deposits on particle surfaces, influences toxicity to the respiratory system, using an advanced in vitro approach.
To emplace surface sulphate salts on particles, via replication of the physicochemical reactions that occur between pristine ash surfaces and volcanic gas, analogue substrates (powdered synthetic volcanic glass and natural pumice) were exposed to SO2 at 500 °C, in a novel Advanced Gas-Ash Reactor, resulting in salt-laden particles. The solubility of surface salt deposits was then assessed by leaching in water and geochemical modelling. A human multicellular lung model was exposed to aerosolised salt-laden and pristine (salt-free) particles, and incubated for 24 h. Cell cultures were subsequently assessed for biological endpoints, including cytotoxicity (lactate dehydrogenase release), oxidative stress (oxidative stress-related gene expression; heme oxygenase 1 and NAD(P)H dehydrogenase [quinone] 1) and its (pro-)inflammatory response (tumour necrosis factor α, interleukin 8 and interleukin 1β at gene and protein levels).
In the lung cell model no significant effects were observed between the pristine and SO2-exposed particles, indicating that the surface salt deposits, and the underlying alterations to the substrate, do not cause acute adverse effects in vitro. Based on the leachate data, the majority of the sulphate salts from the ash surfaces are likely to dissolve in the lungs prior to cellular uptake.
The findings of this study indicate that interaction of volcanic ash with SO2 during ash generation and transport does not significantly affect the respiratory toxicity of volcanic ash in vitro. Therefore, sulphate salts are unlikely a dominant factor controlling variability in in vitro toxicity assessments observed during previous eruption response efforts.
Sustainability has been at the forefront of the environmental research agenda of the integrated anthroposphere, hydrosphere, and biosphere since the last century and will continue to be critically important for future environmental science. However, linking humans and the environment through effective policy remains a major challenge for sustainability research and practice. Here we address this gap using an agent-based model (ABM) for a coupled natural and human systems in the Smoky Hill River Watershed (SHRW), Kansas, USA. For this freshwater-dependent agricultural watershed with a highly variable flow regime influenced by human-induced land-use and climate change, we tested the support for an environmental policy designed to conserve and protect fish biodiversity in the SHRW. We develop a proof of concept interdisciplinary ABM that integrates field data on hydrology, ecology (fish richness), social-psychology (value-belief-norm) and economics, to simulate human agents' decisions to support environmental policy. The mechanism to link human behaviors to environmental changes is the social-psychological sequence identified by the value-belief-norm framework and is informed by hydrological and fish ecology models. Our results indicate that (1) cultural factors influence the decision to support the policy; (2) a mechanism modifying social-psychological factors can influence the decision-making process; (3) there is resistance to environmental policy in the SHRW, even under potentially extreme climate conditions; and (4) the best opportunities for policy acceptance were found immediately after extreme environmental events. The modeling approach presented herein explicitly links biophysical and social science has broad generality for sustainability problems.
Environmental drivers of population vital rates, such as temperature and precipitation, often vary at short time scales, and these fluctuations can have important impacts on population dynamics. However, relationships between survival and environmental conditions are typically modeled at coarse temporal scales, ignoring the role of daily environmental variation in survival. Our goal was to determine the importance of fine-scale temporal variation in survival to population dynamics of stream salmonids. We extended the Cormack–Jolly–Seber model to estimate daily survival rates from seasonal samples of individually marked brook trout (Salvelinus fontinalis) in a stream network. Daily variation in temperature and flow were strongly associated with survival, but relationships varied between juvenile and adult trout and among streams. In all streams, juveniles had higher mortality in warm, low-flow conditions, but in the two larger streams, cold, high-flow conditions also reduced juvenile survival. Adult survival decreased during low flows, particularly in the fall spawning period. Differing survival responses among stream network components to short-term environmental events created shifts in optimal location for maximum survival across life stages, seasons, and years.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2018-0191","usgsCitation":"Childress, E., Nislow, K., Whiteley, A.R., O’Donnell, M., and Letcher, B., 2019, Daily estimates reveal fine-scale temporal and spatial variation in fish survival across a stream network: Canadian Journal of Fisheries and Aquatic Sciences, v. 76, no. 8, p. 1446-1458, https://doi.org/10.1139/cjfas-2018-0191.","productDescription":"13 p.","startPage":"1446","endPage":"1458","ipdsId":"IP-098110","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":377086,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"76","issue":"8","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Childress, Evan S.","contributorId":214287,"corporation":false,"usgs":false,"family":"Childress","given":"Evan S.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":794922,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nislow, Keith","contributorId":201434,"corporation":false,"usgs":false,"family":"Nislow","given":"Keith","affiliations":[{"id":27110,"text":"U.S. Dept of Agriculture, Forest Service","active":true,"usgs":false}],"preferred":false,"id":794925,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Whiteley, Andrew R.","contributorId":150155,"corporation":false,"usgs":false,"family":"Whiteley","given":"Andrew","email":"","middleInitial":"R.","affiliations":[{"id":6932,"text":"University of Massachusetts, Amherst","active":true,"usgs":false}],"preferred":false,"id":794923,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"O’Donnell, Matthew 0000-0002-9089-2377 mjodonnell@usgs.gov","orcid":"https://orcid.org/0000-0002-9089-2377","contributorId":167315,"corporation":false,"usgs":true,"family":"O’Donnell","given":"Matthew","email":"mjodonnell@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":794924,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Letcher, Benjamin 0000-0003-0191-5678 bletcher@usgs.gov","orcid":"https://orcid.org/0000-0003-0191-5678","contributorId":169305,"corporation":false,"usgs":true,"family":"Letcher","given":"Benjamin","email":"bletcher@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":794926,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70212321,"text":"70212321 - 2019 - Geographic-specific capture-recapture models reveal contrasting migration and survival rates of adult horseshoe crabs (Limulus polyphemus)","interactions":[],"lastModifiedDate":"2020-08-14T14:34:26.531968","indexId":"70212321","displayToPublicDate":"2020-07-01T09:26:56","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1584,"text":"Estuaries and Coasts","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Geographic-specific capture-recapture models reveal contrasting migration and survival rates of adult horseshoe crabs (Limulus polyphemus)","title":"Geographic-specific capture-recapture models reveal contrasting migration and survival rates of adult horseshoe crabs (Limulus polyphemus)","docAbstract":"American horseshoe crabs (Limulus polyphemus) have varied migration patterns and harvesting pressure throughout their range, potentially leading to regional differences in population dynamics. Here, a multi-state mark–recapture model was used to estimate annual survival and exchange rates of adult horseshoe crabs across three geographic regions in Long Island, NY (South Shore, North Shore, and Jamaica Bay areas). Under the New York Horseshoe Crab Monitoring program, a total of 22,525 adult horseshoe crabs were tagged and 879 (3.9%) unique recaptures were observed from 2007 to 2016. Model-averaged annual survival in the North Shore population was higher at 68% (95% confidence interval (CI) 61.9–73.4) when compared to the South Shore (56.8%, 95% CI 51.1–62.2) and Jamaica Bay (54.5%, 95% CI 47.0–61.7) regions. Differences in survival between the North Shore and South Shore may reflect the greater harvest pressure directed along the South Shore. Contrary to expectations for a primarily closed region, Jamaica Bay survival was low, but not attributable to reported harvest related activities. Annual movement from the Jamaica Bay into the adjacent South Shore region was 19.8% (95% CI 13.1–28.9), but annual exchange rates ranging from 0.5 to 5.0% were observed between other regions. For example, movement from the South Shore and North Shore into Jamaica Bay was 3.5% (95% CI 2.3–5.9) and 0.5% (95% CI 0.0–1.0), respectively. There was strong support for sex-specific differences in survival, primarily driven by the low survival of females in Jamaica Bay (33.8%, 95% CI 21.1–50.5). Our findings reveal potential management implications, such as regional survival differences within a uniformly managed stock, and net emigration from a predominantly closed to open harvest region reducing the effectiveness of a protected area.
","language":"English","publisher":"Springer","doi":"10.1007/s12237-019-00595-1","usgsCitation":"Bopp, J.J., Sclafani, M., Smith, D.R., McKown, K., Sysak, R., and Cerrato, R., 2019, Geographic-specific capture-recapture models reveal contrasting migration and survival rates of adult horseshoe crabs (Limulus polyphemus): Estuaries and Coasts, v. 42, p. 1570-1585, https://doi.org/10.1007/s12237-019-00595-1.","productDescription":"16 p.","startPage":"1570","endPage":"1585","ipdsId":"IP-106088","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":377519,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Jersey, New York","otherGeospatial":"Jamaica Bay, Long Island, North Shore, South Shore","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.1302490234375,\n 40.06125658140474\n ],\n [\n -73.916015625,\n 40.027614437486655\n ],\n [\n -73.8775634765625,\n 40.49709237269567\n ],\n [\n -72.65808105468749,\n 40.66813955408042\n ],\n [\n -71.74072265625,\n 41.03793062246529\n ],\n [\n -71.9439697265625,\n 41.20345619205131\n ],\n [\n -72.333984375,\n 41.29431726315258\n ],\n [\n -72.894287109375,\n 41.261291493919884\n ],\n [\n -73.49853515625,\n 41.062786068733026\n ],\n [\n -73.8885498046875,\n 40.84290487729676\n ],\n [\n -74.0863037109375,\n 40.66397287638688\n ],\n [\n -74.080810546875,\n 40.576412521044425\n ],\n [\n -74.2730712890625,\n 40.49291502689579\n ],\n [\n -74.234619140625,\n 40.451127265872316\n ],\n [\n -74.0643310546875,\n 40.32141999593439\n ],\n [\n -74.1302490234375,\n 40.06125658140474\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"42","noUsgsAuthors":false,"publicationDate":"2019-07-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Bopp, Justin J.","contributorId":238554,"corporation":false,"usgs":false,"family":"Bopp","given":"Justin","email":"","middleInitial":"J.","affiliations":[{"id":36488,"text":"Stony Brook University","active":true,"usgs":false}],"preferred":false,"id":796384,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sclafani, Matthew","contributorId":238556,"corporation":false,"usgs":false,"family":"Sclafani","given":"Matthew","email":"","affiliations":[{"id":47742,"text":"Cornell Cooperative Extension","active":true,"usgs":false}],"preferred":false,"id":796385,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, David R. 0000-0001-6074-9257 drsmith@usgs.gov","orcid":"https://orcid.org/0000-0001-6074-9257","contributorId":168442,"corporation":false,"usgs":true,"family":"Smith","given":"David","email":"drsmith@usgs.gov","middleInitial":"R.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":796386,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McKown, Kim","contributorId":238557,"corporation":false,"usgs":false,"family":"McKown","given":"Kim","email":"","affiliations":[{"id":47744,"text":"New York Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":796387,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sysak, Rachel","contributorId":238558,"corporation":false,"usgs":false,"family":"Sysak","given":"Rachel","email":"","affiliations":[{"id":13678,"text":"New York State Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":796388,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cerrato, Robert","contributorId":238559,"corporation":false,"usgs":false,"family":"Cerrato","given":"Robert","email":"","affiliations":[{"id":36488,"text":"Stony Brook University","active":true,"usgs":false}],"preferred":false,"id":796389,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70203838,"text":"70203838 - 2019 - Groundwater flow model for Western Chippewa County–Including analysis of water resources related to industrial sand mining and irrigated agriculture","interactions":[],"lastModifiedDate":"2020-05-29T19:13:50.212524","indexId":"70203838","displayToPublicDate":"2020-05-29T14:03:13","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":5959,"text":"Wisconsin Geological and NaturalHistory Survey Bulletin","active":true,"publicationSubtype":{"id":2}},"seriesNumber":"B112","title":"Groundwater flow model for Western Chippewa County–Including analysis of water resources related to industrial sand mining and irrigated agriculture","docAbstract":"A groundwater flow model for western Chippewa County, Wisconsin, was developed by the Wisconsin Geological and Natural History Survey (WGNHS) and the U.S. Geological Survey (USGS) using the computer program MODFLOW. The model is the result of a five-year groundwater study commissioned by Chippewa County in 2012 to evaluate the effects of industrial sand mining and irrigated agriculture on the county’s water resources. The study incorporates existing data and newly acquired data from fieldwork conducted within the study area. The groundwater model may be useful for future investigations, such as evaluation of proposed high-capacity well sites, development of municipal wellhead protection plans, and studies that seek to further quantify surface water-groundwater relationships.
The model conceptualizes the hydrostratigraphy of western Chippewa County as six stacked layers. Each layer is distinct, beginning with unlithified glacial material at the surface, and alternating between sandstones (that act as aquifers) and shale units (that serve as aquitards). The model is bounded below by Precambrian crystalline bedrock and its perimeter was derived from a regional-scale groundwater flow model.
The MODFLOW model represented average conditions during 2011–2013 with “steady-state” assumptions, meaning that simulated water levels do not fluctuate seasonally or from year to year. Steady-state models simplify natural variability, making results of scenario simulations easier to interpret and compare while also maximizing effects of stressors because the simulated stress is always applied (not halted after a few months or years). Model calibration used the parameter estimation code (PEST), and calibration targets included heads (groundwater levels) and streamflows. Calibration focused on 2011–2013 because a large amount of head and streamflow data were available for that period.
The MODFLOW model explicitly simulates all sources and sinks of water, including groundwater/surface-water interaction with streamflow routing. Model input included estimates of aquifer hydraulic conductivity and a spatial groundwater recharge distribution developed using a GIS-based soil-water-balance (SWB) model applied to the model area. Groundwater withdrawals were simulated for 269 high-capacity wells across the entire model domain, which includes western Chippewa County and adjacent portions of Dunn, Barron, and Rusk Counties. Collectively, these wells withdrew about 1.14 million gallons per year between 2011 and 2013.
Once the model was calibrated, it was applied to two distinct scenarios of increased groundwater withdrawals: one evaluating hydrologic effects of more intensive industrial sand mining and the second evaluating the hydrologic effects of more intensive agricultural irrigation practices. Each scenario was developed with input from Chippewa County and a stakeholder group established expressly for this study. The scenarios were designed to represent reasonable future buildout conditions for both mining and irrigated agriculture. The mining scenario underscores the potential hydrologic effects related to changing land-use practices (i.e., hilltops and farmland becoming sand mines), while the irrigated agriculture scenario illustrates the potential hydrologic effects of intensifying existing land-use practices (i.e., installing new wells to irrigate farm fields).
While each scenario evaluated distinctly different conditions, modeling results demonstrated the potential of both scenarios to lower the water table and reduce baseflows in headwater streams within the modeled area. In the case of irrigated agriculture, hydrologic effects were associated directly with groundwater withdrawals. By assuming that irrigation did not decrease, this steady-state simulation represented a sustained future effect. By contrast, hydrologic effects of industrial sand mining were the result of both groundwater withdrawals at mines and land-use changes that effectively reduced recharge to groundwater over distinct phases of active mining. This scenario included a post-mining phase, during which groundwater withdrawals stopped and mined areas were reclaimed to undeveloped prairie grass cover. If reclamation to undeveloped prairie indeed occurs as simulated, long-term increases in the water table and stream baseflows are possible. In this sense, the scenario representing build out of irrigated agriculture led to long-term baseflow declines while the future buildout of industrial sand mining led to declines that dissipated following mine reclamation to undisturbed prairie.
Future investigations in similar hydrogeologic settings may find the following insights gleaned from this study useful:
❚❚ The characterization of hydrogeologic properties, delineation of hydrogeologic units, and calibration of groundwater flow models benefited from incorporation of accurate well construction reports, high-quality borehole geophysical logs, and streamflow gaging data.
❚❚ Infiltration testing performed in active mining areas provided evidence that reducing the degree and extent of compaction and enhancing areas designed to retain and infiltrate stormwater runoff could potentially reduce runoff and increase groundwater recharge.
❚❚ Similarly, reclaiming mined areas to prairie grasses would be expected to reduce runoff and increase groundwater recharge by reducing compaction and improving soil structure and vegetation that can slow runoff and enhance infiltration.
","language":"English","publisher":"Wisconsin Geological and Natural History Survey","usgsCitation":"Parsen, M., Juckem, P.F., Gotkowitz, M., and Fienen, M.N., 2019, Groundwater flow model for Western Chippewa County–Including analysis of water resources related to industrial sand mining and irrigated agriculture: Wisconsin Geological and NaturalHistory Survey Bulletin B112, 74 p.","productDescription":"74 p.","ipdsId":"IP-093476","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":375174,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":375173,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://wgnhs.wisc.edu/pubs/b112/"}],"country":"United States","state":"Wisconsin","county":"Chippewa County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.8511962890625,\n 44.859762688042736\n ],\n [\n -91.31011962890625,\n 44.859762688042736\n ],\n [\n -91.31011962890625,\n 45.55060191034006\n ],\n [\n -91.8511962890625,\n 45.55060191034006\n ],\n [\n -91.8511962890625,\n 44.859762688042736\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Parsen, Michael","contributorId":216283,"corporation":false,"usgs":false,"family":"Parsen","given":"Michael","affiliations":[{"id":39043,"text":"Wisconsin Geological and Natural History Survey","active":true,"usgs":false}],"preferred":false,"id":764401,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Juckem, Paul F. 0000-0002-3613-1761 pfjuckem@usgs.gov","orcid":"https://orcid.org/0000-0002-3613-1761","contributorId":1905,"corporation":false,"usgs":true,"family":"Juckem","given":"Paul","email":"pfjuckem@usgs.gov","middleInitial":"F.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":764400,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gotkowitz, Madeline","contributorId":216284,"corporation":false,"usgs":false,"family":"Gotkowitz","given":"Madeline","affiliations":[{"id":39043,"text":"Wisconsin Geological and Natural History Survey","active":true,"usgs":false}],"preferred":false,"id":764402,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fienen, Michael N. 0000-0002-7756-4651 mnfienen@usgs.gov","orcid":"https://orcid.org/0000-0002-7756-4651","contributorId":171511,"corporation":false,"usgs":true,"family":"Fienen","given":"Michael","email":"mnfienen@usgs.gov","middleInitial":"N.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":764403,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70206903,"text":"sir20195136 - 2019 - Geohydrology and water quality of the unconsolidated aquifers in the Enfield Creek Valley, town of Enfield, Tompkins County, New York","interactions":[],"lastModifiedDate":"2020-05-14T11:35:30.264583","indexId":"sir20195136","displayToPublicDate":"2020-05-13T16:20:00","publicationYear":"2019","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":"2019-5136","displayTitle":"Geohydrology and Water Quality of the Unconsolidated Aquifers in the Enfield Creek Valley, Town of Enfield, Tompkins County, New York","title":"Geohydrology and water quality of the unconsolidated aquifers in the Enfield Creek Valley, town of Enfield, Tompkins County, New York","docAbstract":"From 2013 to 2018, the U.S. Geological Survey, in cooperation with the Town of Enfield and the Tompkins County Planning Department, studied the unconsolidated aquifer in the Enfield Creek Valley in the town of Enfield, Tompkins County, New York. The valley will likely undergo future development as the population of Tompkins County increases and spreads out from the metropolitan areas. The Town of Enfield, Tompkins County, and the New York State Departments of Health and Environmental Conservation need geohydrologic information to help planners develop a more comprehensive approach to water-resource management in Tompkins County.
The Enfield Creek Valley is underlain by an unconfined aquifer that consists of saturated alluvium, alluvial-fan deposits, and ice-contact (kame) sand and gravel. A confined aquifer of discontinuous ice-contact sand and gravel overlies bedrock. Depth to bedrock in the valley ranges from about 50 feet below land surface from just north of the Enfield Creek divide in the northern part of the aquifer to the confluence of Fivemile Creek to at least 140 feet below land surface from Fivemile Creek to where the valley orientation changes from north-south to northwest-southeast. Depth to bedrock is much shallower from the valley orientation change to the southeastern part of the aquifer because Enfield Creek has carved through overlying sediments into bedrock as the creek drops 450 feet into the Cayuga Inlet Valley. A small buried valley running south to north was identified within the Fivemile Creek drainage along the western edge of the town. However, the valley fill consists of glacial till, and no sand-and-gravel aquifer is present.
The unconfined aquifers are recharged by direct infiltration of precipitation, surface runoff, and shallow subsurface flow from hillsides, and by seepage loss from streams overlying the aquifer. The confined aquifers are recharged mostly by precipitation that enters the adjacent valley walls, by groundwater flowing from bordering till or bedrock, and by flow from the bottom of the valley. Also, some recharge may be occurring where confining units are absent or from confining units with sediments of moderate permeability.
Groundwater discharges to Enfield Creek, its tributaries, and wetlands and is lost through evapotranspiration from the water table or is withdrawn from domestic, commercial, and agricultural wells. About 700 individual well owners depend on the unconsolidated aquifers for their water supply. An estimated 28,300,000 gallons per year are withdrawn.
Groundwater samples were collected from eight test wells drilled for this study, and six surface-water samples were collected from five locations on Enfield Creek. Of the eight wells sampled, two were finished in unconfined sand-and-gravel aquifers, two were finished in confined sand-and-gravel aquifers, and four were finished at or near the shale bedrock surface.
Water quality in the study area generally met State and Federal drinking-water standards. However, some samples exceeded maximum contaminant levels for barium (25 percent of samples) and secondary maximum containment levels for chloride (25 percent), dissolved solids (25 percent of samples), iron (70 percent of samples), and manganese (75 percent of samples). Groundwater from 75 percent of the wells sampled for methane had concentrations greater than the Office of Surface Mining Reclamation and Enforcement recommended action level of 10 to 28 milligrams per liter. The two deepest wells sampled, TM1075 and TM1077, had the highest specific conductance, chloride, and sodium concentrations of all wells sampled. The chloride/bromide ratios of these samples suggest the source may represent a mixture of saline formation waters with shallow dilute groundwater and may receive recharge contribution from two tributaries overlying bedrock to the west and southwest of the aquifer. In general, the highest yields are from wells completed within about 50 feet below land surface, which may tap either type of aquifer.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195136","collaboration":"Prepared in cooperation with the Town of Enfield and the Tompkins County Planning Department","usgsCitation":"Fisher, B.N., Heisig, P.M., and Kappel, W.M., 2019, Geohydrology and water quality of the unconsolidated aquifers in the Enfield Creek Valley, Town of Enfield, Tompkins County, New York (ver. 1.1, May 2020): U.S. Geological Survey Scientific Investigations Report 2019–5136, 52 p., https://doi.org/10.3133/sir20195136.","productDescription":"Report: vi, 52 p.; 3 Data Releases","numberOfPages":"62","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-103465","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":370147,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZFTDFY","text":"USGS data release","linkHelpText":"A horizontal-to-vertical spectral ratio soundings and depth-to-bedrock data for geohydrology and water quality investigation of the unconsolidated aquifers in the Enfield Creek Valley, town of Enfield, Tompkins County, New York, April 2013–August 2015"},{"id":370148,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90FOTQV","text":"USGS data release","linkHelpText":"Records of selected wells for geohydrology and water quality investigation of the unconsolidated aquifers in the Enfield Creek Valley, town of Enfield, Tompkins County, New York, 2013–2018"},{"id":374790,"rank":5,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5136/coverthb2.jpg"},{"id":374788,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9T3U63J","text":"USGS data release","linkHelpText":"Geospatial datasets for the hydrogeology and water quality of the unconsolidated aquifers in the Enfield Creek Valley, Town of Enfield, Tompkins County, New York"},{"id":374789,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2019/5136/versionhist.txt","text":"Version history","size":"1.35 KB","linkFileType":{"id":2,"text":"txt"}},{"id":374792,"rank":6,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5136/sir20195136.pdf","text":"Report","size":"5.95 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"New York","county":"Tompkins County","otherGeospatial":"Enfield Creek Valley, Town of Enfield","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.69281005859375,\n 42.47817430242155\n ],\n [\n -76.68731689453125,\n 42.39050147746088\n ],\n [\n -76.5692138671875,\n 42.39405131362432\n ],\n [\n -76.57230377197266,\n 42.48222557002593\n ],\n [\n -76.69281005859375,\n 42.47817430242155\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.1: May 13, 2020; Version 1.0 December 13, 2019","contact":"Director, New York Water Science Center
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Multi-species approaches to wildlife management have become commonplace and purport to benefit entire biological communities. These strategies aim to manage different, often taxonomically distant species under a single regime based on shared habitat associations and/or co-occurrence in the landscape. We tested the efficacy of multi-species management in the context of creating and maintaining early-successional forest cover types using two species of migratory birds that breed in eastern North America and are each the focus of intensive, concurrent, and overlapping management. American woodcock (Scolopax minor) and golden-winged warblers (Vermivora chrysoptera) breed in similar diverse-forest landscapes. Each species purportedly benefits from management for the other species and both are often used as flagship species for the creation of young forest and the conservation of associated avian communities. However, the landscape-species relationships that drive reproductive success and population stability in these species have not been explicitly compared. Here, we use previously published spatially-explicit models of productivity (the number of juveniles raised to a biologically significant milestone) to identify the relationship(s) between productivity of American woodcock and golden-winged warblers across a shared landscape. We found productivity to be negatively associated between these species on the same landscape at all spatial scales we modelled (1 m2–100 ha). Our results suggest that, with regards to productivity, American woodcock and golden-winged warblers have opposing relationships with the composition of the landscapes in which they coexist and therefore should not be assumed to benefit similarly from any individual management action at any relevant spatial scale.
We estimated detection probabilities of bird carcasses along sandy beaches and in marsh edge habitats in the northern Gulf of Mexico to help inform models of bird mortality associated with the Deepwater Horizon oil spill. We also explored factors that may influence detection probability, such as carcass size, amount of scavenging, location on the beach, habitat type, and distance into the marsh. Detection probability for medium-sized carcasses (200–500 g) ranged from 0.82 (SE = 0.09) to 0.93 (SE = 0.04) along sandy beaches. Within sandy beaches, we found that intact/slightly scavenged carcasses were easier to detect than heavily scavenged ones and did not find strong effects of location on the beach on detection probability. We estimated detection rate for each combination of scavenging state, carcass size, and position along sandy beaches. In marsh edge habitats, detection ranged from 0.04 (SE = 0.04) to 0.86 (SE = 0.10), with detection rates rapidly increasing from small (< 200 g) to medium carcass sizes and leveling off between medium and extra-large (> 1000 g) carcasses regardless of vegetation type (Spartina or Phragmites). Carcasses of all sizes were generally harder to locate in Spartina-dominated marshes than in Phragmites-dominated ones. A subset of the data for which we could adequately assess the effect of distance into the marsh indicated that detection rates generally declined the farther a carcass was into marsh vegetation. Based on power analyses, our ability to identify predictors that influence detection rates would be higher with larger numbers of carcasses, greater numbers of search trials per carcass, or more balanced sampling distributions across predictor values.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s10661-019-7924-z","usgsCitation":"Zimmerman, G.S., Varela, V., and Yee, J.L., 2019, Detection probabilities of bird carcasses along sandy beaches and marsh edges in the northern Gulf of Mexico: Environmental Monitoring and Assessment, v. 191, no. suppl 4, 816, 15 p., https://doi.org/10.1007/s10661-019-7924-z.","productDescription":"816, 15 p.","ipdsId":"IP-094446","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":458847,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10661-019-7924-z","text":"Publisher Index Page"},{"id":375518,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Florida, Louisiana, Mississippi, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.2352294921875,\n 29.544787796199465\n ],\n [\n -94.0814208984375,\n 29.640320395351402\n ],\n [\n -93.61450195312499,\n 29.72145191669099\n ],\n [\n -93.218994140625,\n 29.754839972510933\n ],\n [\n -92.5213623046875,\n 29.559123451577964\n ],\n [\n -92.2247314453125,\n 29.501768632523262\n ],\n [\n -92.08740234375,\n 29.559123451577964\n ],\n [\n -91.8841552734375,\n 29.482643134466617\n ],\n [\n -91.1810302734375,\n 29.204918463909035\n ],\n [\n -90.5438232421875,\n 29.16655229520015\n ],\n [\n -90.3076171875,\n 29.28160772298835\n ],\n [\n -90.2691650390625,\n 29.017748018496047\n ],\n [\n -89.9395751953125,\n 29.176145182559758\n ],\n [\n -89.70886230468749,\n 29.3295093083647\n ],\n [\n -89.132080078125,\n 28.93124697186731\n ],\n [\n -88.978271484375,\n 29.257648503615542\n ],\n [\n -89.1485595703125,\n 30.12612436422458\n ],\n [\n -89.1925048828125,\n 30.311245603935003\n ],\n [\n -88.65966796875,\n 30.306503259848835\n ],\n [\n -88.3245849609375,\n 30.372875188118016\n ],\n [\n -88.1927490234375,\n 30.306503259848835\n ],\n [\n -87.77526855468749,\n 30.14512718337613\n ],\n [\n -87.3797607421875,\n 30.292274851024256\n ],\n [\n -86.517333984375,\n 30.358656420788783\n ],\n [\n -85.8251953125,\n 30.183121842195515\n ],\n [\n -85.45166015624999,\n 29.935895213372444\n ],\n [\n -85.3143310546875,\n 30.050076521698735\n ],\n [\n -85.7977294921875,\n 30.37761431777479\n ],\n [\n -86.3031005859375,\n 30.519681272749402\n ],\n [\n -87.0172119140625,\n 30.62845887475364\n ],\n [\n -87.4566650390625,\n 30.519681272749402\n ],\n [\n -88.39050292968749,\n 30.50548389892728\n ],\n [\n -89.1925048828125,\n 30.48181700282728\n ],\n [\n -89.62646484375,\n 30.330212685432734\n ],\n [\n -90.362548828125,\n 29.859701442126756\n ],\n [\n -90.68115234375,\n 29.506549442788593\n ],\n [\n -91.2579345703125,\n 29.654642479663647\n ],\n [\n -91.7962646484375,\n 29.92637417863576\n ],\n [\n -92.30712890625,\n 29.78821690967894\n ],\n [\n -93.3673095703125,\n 29.88351825335318\n ],\n [\n -94.47143554687499,\n 29.716681287231072\n ],\n [\n -94.2352294921875,\n 29.544787796199465\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"191","issue":"suppl 4","noUsgsAuthors":false,"publicationDate":"2020-03-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Zimmerman, Guthrie S.","contributorId":42473,"corporation":false,"usgs":false,"family":"Zimmerman","given":"Guthrie","email":"","middleInitial":"S.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":790710,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Varela, Veronica","contributorId":225184,"corporation":false,"usgs":false,"family":"Varela","given":"Veronica","email":"","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":790711,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yee, Julie L. 0000-0003-1782-157X julie_yee@usgs.gov","orcid":"https://orcid.org/0000-0003-1782-157X","contributorId":3246,"corporation":false,"usgs":true,"family":"Yee","given":"Julie","email":"julie_yee@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":790712,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70206090,"text":"sir20195114 - 2019 - Spatially referenced models of streamflow and nitrogen, phosphorus, and suspended-sediment loads in streams of the midwestern United States","interactions":[],"lastModifiedDate":"2020-02-04T06:07:34","indexId":"sir20195114","displayToPublicDate":"2020-02-04T07:20:00","publicationYear":"2019","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":"2019-5114","displayTitle":"Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Midwestern United States","title":"Spatially referenced models of streamflow and nitrogen, phosphorus, and suspended-sediment loads in streams of the midwestern United States","docAbstract":"In this report, SPAtially Referenced Regression On Watershed attributes (SPARROW) models developed to describe long-term (2000–14) mean-annual streamflow, total nitrogen (TN), total phosphorus (TP), and suspended-sediment (SS) transport in streams of the Midwestern part of the United States (the Mississippi River, Great Lakes, and Red River of the North Basins) are described. The nutrient and suspended-sediment models have a base year of 2012, which means they were developed based on source inputs and management practices similar to those existing during or near 2012 and average hydrological conditions detrended to 2012 (2000–14), whereas the streamflow model has base years of 2000–14, which means it was developed based on the average input precipitation minus actual evapotranspiration from 2000 to 2014. In developing the models, several updates and improvements were made to the data inputs and statistical approaches used to calibrate/develop the models from those used in the previous 2002 SPARROW models. The 2012 SPARROW models were constructed using a higher resolution stream network, which resulted in a mean catchment size of 2.7 square kilometers compared to 480 square kilometers in the 2002 models; more detailed and updated wastewater treatment plant contribution estimates; inputs from background phosphorus sources that were not included in the 2002 model; and more accurate loads for calibration that were computed using a modified Beale ratio-estimator technique whenever no trend in load was determined. Statistical approaches were added to compensate for the unequal effect of each monitoring site during the calibration process by adjusting for the fraction of the basin included in other upstream monitored sites (nested share) and thinning the calibration sites if a negative statistical correlation between nearby sites was determined.
Results from 2012 SPARROW models describe how much of each water, TN, TP, and SS source was delivered to the stream network, and the major landscape factors that affected their delivery. Atmospheric deposition and natural (background) sources of TN and TP, respectively, were the dominant sources in anthropogenically unaffected areas (especially in the Rocky Mountains and north-central areas of the Midwest), whereas fertilizers, manure, and fixation were dominant sources in agricultural areas, especially in the Corn Belt and near the Mississippi River. Urban sources of TN and TP were typically localized, but they were still important for some large areas, especially the Lake Erie Basin. All of the land-to-water delivery variables in the nutrient and sediment SPARROW models, such as runoff, soil erodibility, basin slope, and the amount of tile drains, are commonly included in process-driven models. In the SPARROW TN and TP models, best management practices (BMPs) reduced the delivery of these nutrients to streams.
Long-term mean-annual flows and nutrient and sediment loads were simulated in streams throughout the Midwest. The simulated flows from the SPARROW flow model were used in the SPARROW TN, TP, and SS models to help describe nutrient and sediment transport from the watershed and through the stream network. Outputs from the TN, TP, and SS models describe loads and yields of these constituents throughout the Midwest, and from major drainage basins throughout the Midwest. Highest TN, TP, and SS yields and delivered yields were from the Lake Erie, Ohio River, Upper Mississippi River, and Lower Mississippi River Basins, whereas lowest yields were spread over most other areas. Losses during downstream delivery resulted in part of the TN, TP, and SS that reach the stream network not reaching the downstream receiving bodies: 14, 15, and 28 percent of the TN, TP, and SS, respectively, are lost during delivery to the Great Lakes and 19, 23, and 52 percent of the TN, TP, and SS, respectively, are lost during delivery to the Gulf of Mexico. The largest losses of nutrients and sediments during transport were in the Missouri and Arkansas River Basins.
Information from these SPARROW models can help guide nutrient and sediment reduction strategies throughout the Midwest. Model results provide information on what may be the most appropriate general type of actions to reduce total loading by describing the relative importance of each source, and where to most efficiently place the efforts to reduce loading by describing the distribution of nutrient and sediment loading. By implementing management efforts addressing the major sources of the loads in areas contributing the highest loads, it may be possible to reduce nutrient loading throughout the Mississippi River Basin and thus reduce the size of the hypoxic zone in the Gulf of Mexico; reduce nutrient loading into lakes, and thus reduce the occurrence of harmful algal blooms; and reduce sediment losses, and thus improve the benthic habitat in streams and rivers throughout the Midwest.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195114","collaboration":"National Water Quality Program","usgsCitation":"Robertson, D.M., and Saad, D.A., 2019, Spatially referenced models of streamflow and nitrogen, phosphorus, and suspended-sediment loads in streams of the Midwestern United States: U.S. Geological Survey Scientific Investigations Report 2019–5114, 74 p. including 5 appendixes, https://doi.org/10.3133/sir20195114.","productDescription":"Report: ix, 74 p.; Data Release","numberOfPages":"88","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-103244","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":370714,"rank":6,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195135","text":"SIR 2019–5135","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Southeastern United States"},{"id":370711,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195106","text":"SIR 2019–5106","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Southwestern United States"},{"id":370712,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195112","text":"SIR 2019–5112","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Pacific Region of the United States"},{"id":371971,"rank":8,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5114/sir20195114.pdf","text":"Report","size":"43.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5114"},{"id":370371,"rank":2,"type":{"id":4,"text":"Application Site"},"url":"https://sparrow.wim.usgs.gov/sparrow-midwest-2012/","text":"Mapping application","linkHelpText":"– Online mapping tool to explore 2012 SPARROW Models"},{"id":370713,"rank":5,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195118","text":"SIR 2019–5118","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Northeastern United States"},{"id":370369,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93QMXC9","text":"USGS data release","description":"USGS Data Release","linkHelpText":"SPARROW model inputs and simulated streamflow, nutrient and suspended-sediment loads in streams of the Midwestern United States, 2012 base year"},{"id":370914,"rank":7,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5114/coverthb3.jpg"}],"otherGeospatial":"Midwestern United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.11328125,\n 44.213709909702054\n ],\n [\n -79.27734374999999,\n 43.389081939117496\n ],\n [\n -79.541015625,\n 42.61779143282346\n ],\n [\n -82.529296875,\n 41.44272637767212\n ],\n [\n -82.177734375,\n 43.068887774169625\n ],\n [\n -82.705078125,\n 45.02695045318546\n ],\n [\n -84.287109375,\n 46.6795944656402\n ],\n [\n -90,\n 47.931066347509784\n ],\n [\n -94.5703125,\n 49.210420445650286\n ],\n [\n -95.361328125,\n 49.26780455063753\n ],\n [\n -95.44921875,\n 48.922499263758255\n ],\n [\n -114.2578125,\n 49.095452162534826\n ],\n [\n -113.90625,\n 46.73986059969267\n ],\n [\n -112.236328125,\n 45.336701909968134\n ],\n [\n -110.12695312499999,\n 42.48830197960227\n ],\n [\n -106.69921875,\n 38.06539235133249\n ],\n [\n -101.42578124999999,\n 32.99023555965106\n ],\n [\n -97.20703125,\n 28.536274512989916\n ],\n [\n -92.900390625,\n 29.305561325527698\n ],\n [\n -89.912109375,\n 33.063924198120645\n ],\n [\n -79.541015625,\n 39.774769485295465\n ],\n [\n -76.11328125,\n 44.213709909702054\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"NAWQA Science Team
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 413
Reston, VA 20192–0002
Spatially Referenced Regression On Watershed attributes (SPARROW) models were applied to describe and estimate mean-annual streamflow and transport of total nitrogen (TN), total phosphorus (TP), and suspended sediment (SS) in streams and delivered to coastal waters of the southeastern United States on the basis of inputs and management practices centered near 2012, the base year of the model. Previously published TN and TP models for 2002 served as a starting point and reference for comparison. The datasets developed for the 2012 models not only represent updates of previous conditions but also incorporate new approaches for characterizing sources and transport processes that were not available for previous models.
Variability in streamflow across the southeastern United States was explained as a function of precipitation adjusted for evapotranspiration, spring discharge, and municipal and domestic wastewater discharges to streams. Results from the streamflow model were used as input to the water-quality SPARROW models, and areas with large streamflow prediction errors—urban areas and karst areas—were used to provide guidance on where additional data are needed to improve routing of flow.
Variability in TN transport in Southeast streams was explained by the following five sources in order of decreasing mass contribution to streams: atmospheric deposition, agricultural fertilizer, municipal wastewater, manure from livestock, and urban land. Variable rates of TN delivery from source to stream were attributed to variation among catchments in climate, soil texture, and vegetative cover, including the extent of cover crops in the watershed. Variability in TP transport in Southeast streams was explained by the following six sources in order of decreasing mass contribution to streams: parent-rock minerals, urban land, manure from livestock, municipal wastewater, agricultural fertilizer, and phosphate mining. Varying rates of TP delivery were attributed to variation in climate, soil erodibility, depth to water table, and the extent of conservation tillage practices in the watershed.
Variability in SS transport in Southeast streams was explained by variable sediment export rates for different combinations of land cover and geologic setting (for upland sources of sediment) and by gains in stream power caused by longitudinal changes in channel hydraulics (for channel sources of sediment). Sediment yields for the transitional land cover (shrub, scrub, herbaceous, and barren) varied widely depending on geologic setting and on agricultural land cover. Varying rates of SS delivery, like those for TP, were attributed to variation in climate, soil erodibility, and the extent of conservation tillage practices in the watershed, as well as to areal extent of canopy land cover in the 100-meter buffer along the channel. Relatively large uncertainty, compared to the other three models, for almost all the SS source coefficients indicates the need for caution when interpreting the results from the sediment model.
TN, TP, and SS inputs to streams from sources were balanced in the models with losses from physical processes in streams and reservoirs and with water withdrawals. The losses in streams and reservoirs along with withdrawals removed 35, 44, and 65 percent of the TN, TP, and SS load, respectively, that entered streams before reaching coastal waters.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195135","collaboration":"National Water Quality Program","usgsCitation":"Hoos, A.B., and Roland, V.L. II, 2019, Spatially referenced models of streamflow and nitrogen, phosphorus, and suspended-sediment loads in the Southeastern United States: U.S. Geological Survey Scientific Investigations Report 2019–5135, 91 p., https://doi.org/10.3133/sir20195135.","productDescription":"Report: xi, 87 p.; Data Release; HTML","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-101532","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":370725,"rank":5,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195114","text":"SIR 2019–5114","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Midwestern United States"},{"id":371973,"rank":8,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5135/sir20195135.pdf","text":"Report","size":"10.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5135"},{"id":370724,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195112","text":"SIR 2019–5112","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Pacific Region of the United States"},{"id":370723,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195106","text":"SIR 2019–5106","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Southwestern United States"},{"id":370721,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9A682GW","text":"USGS data release","description":"USGS Data Release","linkHelpText":"SPARROW model inputs and simulated streamflow, nutrient and suspended-sediment loads in streams of the Southeastern United States, 2012 base year"},{"id":370722,"rank":2,"type":{"id":4,"text":"Application Site"},"url":"https://sparrow.wim.usgs.gov/sparrow-southeast-2012/","text":"Mapping application","linkHelpText":"– Online mapping tool to explore 2012 SPARROW Models"},{"id":370726,"rank":6,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195118","text":"SIR 2019–5118","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Northeastern United States"},{"id":371031,"rank":7,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5135/coverthb3.jpg"}],"otherGeospatial":"Southeastern United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.2890625,\n 37.19533058280065\n ],\n [\n -81.34277343749999,\n 37.47485808497102\n ],\n [\n -83.9794921875,\n 34.95799531086792\n ],\n [\n -88.8134765625,\n 33.8339199536547\n ],\n [\n -90.1318359375,\n 33.43144133557529\n ],\n [\n -90.2197265625,\n 30.44867367928756\n ],\n [\n -88.154296875,\n 30.29701788337205\n ],\n [\n -85.69335937499999,\n 29.878755346037977\n ],\n [\n -84.72656249999999,\n 29.49698759653577\n ],\n [\n -84.111328125,\n 29.84064389983441\n ],\n [\n -82.96875,\n 27.955591004642553\n ],\n [\n -82.5732421875,\n 26.54922257769204\n ],\n [\n -80.9033203125,\n 25.20494115356912\n ],\n [\n -80.0244140625,\n 25.3241665257384\n ],\n [\n -79.8486328125,\n 27.527758206861886\n ],\n [\n -80.85937499999999,\n 30.031055426540206\n ],\n [\n -80.771484375,\n 31.615965936476076\n ],\n [\n -78.57421875,\n 33.50475906922609\n ],\n [\n -77.16796875,\n 33.97980872872457\n ],\n [\n -76.11328125,\n 34.95799531086792\n ],\n [\n -75.41015624999999,\n 35.96022296929667\n ],\n [\n -75.8056640625,\n 36.80928470205937\n ],\n [\n -76.2890625,\n 37.19533058280065\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"NAWQA Science Team
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 413
Reston, VA 20192–0002
SPAtially Referenced Regression On Watershed attributes (SPARROW) models were developed to quantify and improve the understanding of the sources, fate, and transport of nitrogen, phosphorus, and suspended sediment in the northeastern United States. Excessive nutrients and suspended sediment from upland watersheds and tributary streams have contributed to ecological and economic degradation of northeastern surface waters. Recent efforts to reduce the flux of nutrients and suspended sediment in northeastern streams and to downstream estuaries have met with mixed results, and expected ecological improvements have been observed in some areas but not in others. Effective watershed management and restoration to improve surface-water quality are complicated by the multitude of nutrient sources in the Northeast and the multitude of natural and human landscape processes affecting the delivery of nutrients and suspended sediment from upland areas to and within surface waters. Individual models were constructed representing streamflow and the loads of total nitrogen, total phosphorus, and suspended sediment from watersheds draining to the Atlantic Ocean from southern Virginia through Maine.
Northeastern streams contribute 303,000 metric tons (t) of nitrogen, 25,300 t of phosphorus, and 14,700,000 t of suspended sediment, annually (on average), to waters along the Atlantic Coast of North America. Although atmospheric deposition and natural mineral erosion contribute to nitrogen and phosphorus loads, respectively, in northeastern streams, most of the contributions are attributable to urban or agricultural sources. Within the Northeast, average yields of nutrients are therefore generally greater from densely populated or intensively cultivated areas of the mid-Atlantic region, the Hudson, Mohawk, and Connecticut River valleys, and the coastal areas of southern New England than in predominantly forested areas such as northern New England. Average upland sediment yields are similarly greater from agricultural areas than from urban or forested areas and are therefore generally greatest in areas yielding the greatest nutrients. Landscape conditions that are significant to nitrogen delivery from uplands to streams likely reflect the importance of groundwater transport in carbonate settings and of denitrification for removing nitrogen from uplands. Nitrogen losses to streams in agricultural areas are apparently mitigated by the use of cover crops but are exacerbated by the use of conservation tillage or no-till practices. The transport of phosphorus and suspended sediment from uplands to streams is greater in areas of more erodible soils but mitigated in agricultural areas with greater use of conservation tillage or no-till practices. Loads of nutrients and suspended sediment are significantly reduced within the stream network in impounded reaches, and nitrogen load is also significantly reduced in small flowing reaches.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195118","collaboration":"National Water Quality Program","usgsCitation":"Ator, S.W., 2019, Spatially referenced models of streamflow and nitrogen, phosphorus, and suspended-sediment loads in streams of the Northeastern United States: U.S. Geological Survey Scientific Investigations Report 2019–5118, 57 p., https://doi.org/10.3133/sir20195118.","productDescription":"Report: ix, 57 p.; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-103253","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":371972,"rank":8,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5118/sir20195118.pdf","text":"Report","size":"31.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5118"},{"id":370715,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195106","text":"SIR 2019–5106","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Southwestern United States"},{"id":370717,"rank":6,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195114","text":"SIR 2019–5114","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Midwestern United States"},{"id":370376,"rank":3,"type":{"id":4,"text":"Application Site"},"url":"https://sparrow.wim.usgs.gov/sparrow-northeast-2012/","text":"Mapping application","linkHelpText":"– Online mapping tool to explore 2012 SPARROW Models"},{"id":370375,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NKNVQO","text":"USGS data release","description":"USGS Data Release","linkHelpText":"SPARROW model inputs and simulated streamflow, nutrient and suspended-sediment loads in streams of the Northeastern United States, 2012 base year"},{"id":370917,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5118/coverthb4.jpg"},{"id":370716,"rank":5,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195112","text":"SIR 2019–5112","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Pacific Region of the United States"},{"id":370718,"rank":7,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195135","text":"SIR 2019–5135","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Southeastern United States"}],"otherGeospatial":"Northeastern United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.81640625,\n 37.43997405227057\n ],\n [\n -75.05859375,\n 37.37015718405753\n ],\n [\n -73.564453125,\n 40.38002840251183\n ],\n [\n -71.455078125,\n 40.64730356252251\n ],\n [\n -70.048828125,\n 42.032974332441405\n ],\n [\n -70.48828125,\n 43.32517767999296\n ],\n [\n -69.169921875,\n 43.77109381775651\n ],\n [\n -66.884765625,\n 44.77793589631623\n ],\n [\n -68.02734375,\n 47.21956811231547\n ],\n [\n -69.43359375,\n 47.338822694822\n ],\n [\n -70.13671875,\n 45.521743896993634\n ],\n [\n -74.970703125,\n 44.84029065139799\n ],\n [\n -77.080078125,\n 43.32517767999296\n ],\n [\n -79.8046875,\n 40.38002840251183\n ],\n [\n -82.001953125,\n 38.61687046392973\n ],\n [\n -76.81640625,\n 37.43997405227057\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"NAWQA Science Team
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 413
Reston, VA 20192–0002
Although spatial information describing the supply and quality of surface water is critical for managing water resources for human uses and for ecological health, monitoring is expensive and cannot typically be done over large scales or in all streams or waterbodies. To address the need for such data, the U.S. Geological Survey developed SPAtially Referenced Regression On Watershed attributes (SPARROW) for the Pacific region of the U.S. for streamflow and three water-quality constituents–total nitrogen, total phosphorus, and suspended sediment, based on a decadal time frame centered on the year 2012. The domain for these models included the Columbia River basin, the Puget Sound, the coastal drainages of Washington, Oregon, and California, and the Central Valley of California. Landscape runoff (represented by the difference between precipitation and evapotranspiration) was the largest source of streamflow, wastewater discharge, and atmospheric deposition were the largest contributors to total nitrogen yield from the Pacific region, wastewater discharge was the largest contributor to total phosphorus yield, and forest land was the largest contributor to suspended-sediment yield. Watersheds with relatively high water yields also generally had relatively high yields of total nitrogen, total phosphorous, and suspended sediment–except where there were large contributions from developed land and wastewater discharge.
The data used in this study, including many that improved upon existing national data or were compiled specifically for the Pacific region, characterized the complex hydrologic and water-quality conditions in the region more completely than previous models. By using these new datasets, this investigation was able to account for the complex network of water diversions and transfers, quantify the contribution of nutrients from different sources of livestock manure, discern a signal from unpaved logging roads in the suspended-sediment yields from forested coastal watersheds, show how recent wildfire disturbance influences phosphorus and sediment delivery to streams, and how sediment delivery to streams is also sensitive to the intensity of cattle grazing. The results from this study could complement research and inform water-quality management activities in the Pacific region. Examples might include identifying potentially impaired waterbodies and guiding remediation efforts where impairment has been documented, explaining the spatial patterns in harmful algal blooms, and providing estimates of sediment and nutrient loadings to Pacific coast estuaries where such data are scarce or non-existent.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195112","collaboration":"National Water Quality Program","usgsCitation":"Wise, D.R., 2019, Spatially referenced models of streamflow and nitrogen, phosphorus, and suspended-sediment loads in streams of the Pacific region of the United States (ver. 1.1, June 2020): U.S. Geological Survey Scientific Investigations Report 2019-5112, 64 p., https://doi.org/10.3133/sir20195112.","productDescription":"Report: x, 64 p.; Data Release; Application Site; Companion Files; Version History; Read Me","numberOfPages":"78","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-103780","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":437240,"rank":11,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9V1RY3N","text":"USGS data release","linkHelpText":"Application of nutrients generated by non-cattle livestock to 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Given the predicted imbalance between water supply and demand in the Southwest region of the United States, and the widespread problems with excessive nutrients and suspended sediment, there is a growing need to quantify current streamflow and water quality conditions throughout the region. Furthermore, current monitoring stations exist at a limited number of locations, and many streams lack streamflow and water quality information. SPAtially Referenced Regression On Watershed attributes (SPARROW) models were developed for hydrologic conditions representative of 2012 in order to understand how climate, land use, and other landscape characteristics control the yields of water, total nitrogen, total phosphorus, and suspended sediment across the Southwest region. The calibration data (mean annual streamflow and loads) for each of the four SPARROW models were based on continuous streamflow and discrete water-quality observations from throughout the region. Explanatory variables for the models consisted of regional datasets representing a range of potential sources of streamflow, nitrogen, phosphorous, and sediment, and processes that control the transport from land to water and attenuate loads within streams and waterbodies. Calibration and explanatory data were referenced to a surface water drainage network that allowed for routing and transport of water and loads through the region. The model results showed that wastewater discharge is the largest contributor to total nitrogen and total phosphorus yield from the Southwest region and forest land is the largest contributor to suspended-sediment yield, but that other sources such as atmospheric nitrogen deposition, agricultural runoff, and runoff from developed land are locally important across the region. The results from this study could complement research and inform water-quality management activities in the Southwest region. Examples might include identifying potentially impaired waterbodies and guiding remediation efforts where impairment has been documented, explaining the spatial patterns in harmful algal blooms, and providing estimates of sediment and nutrient loadings where such data are scarce or non-existent.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195106","collaboration":"National Water Quality Program","usgsCitation":"Wise, D.R., Anning, D.W., and Miller, O.L., 2019, Spatially referenced models of streamflow and nitrogen, phosphorus, and suspended-sediment transport in streams of the southwestern United States (ver. 1.1, June 2020): U.S. Geological Survey Scientific Investigations Report 2019-5106, 66 p., https://doi.org/10.3133/sir20195106.","productDescription":"Report: viii, 66 p.; Data Release","numberOfPages":"78","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-105772","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":437233,"rank":10,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94EKLPP","text":"USGS data release","linkHelpText":"SPARROW model inputs and simulated streamflow, nutrient and suspended-sediment loads in streams of the Southwestern United States, 2012 Base Year (ver. 2.0, October 2020)"},{"id":370350,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5106/coverthb.jpg"},{"id":371969,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5106/sir20195106.pdf","text":"Report","size":"21.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5106"},{"id":370352,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GFLBG8","text":"USGS data release","description":"USGS Data Release","linkHelpText":"SPARROW model inputs and simulated streamflow, nutrient and suspended-sediment loads in streams of the Southwestern United States, 2012 base year"},{"id":370377,"rank":4,"type":{"id":4,"text":"Application Site"},"url":"https://sparrow.wim.usgs.gov/sparrow-southwest-2012","text":"Mapping application","linkHelpText":"– Online mapping tool to explore 2012 SPARROW Models"},{"id":370703,"rank":5,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195112","text":"SIR 2019–5112","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Pacific Region of the United States"},{"id":370704,"rank":6,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195114","text":"SIR 2019–5114","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Midwestern United States"},{"id":370705,"rank":7,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195118","text":"SIR 2019–5118","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Northeastern United States"},{"id":376099,"rank":9,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2019/5106/VersionHist.txt"},{"id":370706,"rank":8,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195135","text":"SIR 2019–5135","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Southeastern United States"}],"country":"United States","otherGeospatial":"Southwestern United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.927734375,\n 32.39851580247402\n ],\n [\n -114.873046875,\n 32.76880048488168\n ],\n [\n -111.09374999999999,\n 31.203404950917395\n ],\n [\n -108.28125,\n 31.052933985705163\n ],\n [\n -108.19335937499999,\n 32.02670629333614\n ],\n [\n -105.8203125,\n 30.977609093348686\n ],\n [\n -103.447265625,\n 28.76765910569123\n ],\n [\n -102.83203125,\n 29.6880527498568\n ],\n [\n -101.689453125,\n 29.76437737516313\n ],\n [\n -100.1953125,\n 27.916766641249065\n ],\n [\n -98.349609375,\n 26.115985925333536\n ],\n [\n -97.20703125,\n 26.509904531413927\n ],\n [\n -96.767578125,\n 28.22697003891834\n ],\n [\n -93.515625,\n 29.53522956294847\n ],\n [\n -99.49218749999999,\n 33.87041555094183\n ],\n [\n -103.623046875,\n 38.8225909761771\n ],\n [\n -104.94140625,\n 40.245991504199026\n ],\n [\n -111.97265625,\n 42.94033923363181\n ],\n [\n -113.90625,\n 41.376808565702355\n ],\n [\n -116.806640625,\n 41.77131167976407\n ],\n [\n -118.740234375,\n 44.213709909702054\n ],\n [\n -120.498046875,\n 44.59046718130883\n ],\n [\n -120.84960937499999,\n 41.44272637767212\n ],\n [\n -118.65234374999999,\n 36.10237644873644\n ],\n [\n -115.927734375,\n 32.39851580247402\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: January 7, 2020; Version 1.1: June 2, 2020","contact":"NAWQA Science Team
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Bulletin 17C (B17C) recommends fitting the log-Pearson Type III (LP−III) distribution to a series of annual peak flows at a streamgage by using the method of moments. The third moment, the skewness coefficient (or skew), is important because the magnitudes of annual exceedance probability (AEP) flows estimated by using the LP−III distribution are affected by the skew; interest is focused on the right-hand tail of the distribution, which represents the larger annual peak flows that correspond to small AEPs. For streamgages having modest record lengths, the skew is sensitive to extreme events like large floods, which cause a sample to be highly asymmetrical or “skewed.” For this reason, B17C recommends using a weighted-average skew computed from the station skew for a given streamgage and a regional skew. This report generates an estimate of regional skew for a study area encompassing most of the Great Lakes Basin (hydrologic unit 04) and part of the Ohio River Basin (hydrologic unit 05). A total of 551 candidate streamgages that were unaffected by extensive regulation, diversion, urbanization, or channelization were considered for use in the skew analysis; after screening for redundancy and pseudo record length greater than 36 years, 368 streamgages were selected for use in the study. Flood frequencies for candidate streamgages were analyzed by employing the Expected Moments Algorithm, which extends the method of moments so that it can accommodate interval, censored, and historic/paleo flow data, as well as the Multiple Grubbs-Beck test to identify potentially influential low floods in the data series. Bayesian weighted least squares/Bayesian generalized least squares regression was used to develop a regional skew model for the study area that would incorporate possible variables (basin characteristics) to explain the variation in skew in the study area. Twelve basin characteristics were considered as possible explanatory variables; however, none produced a pseudo coefficient of determination greater than 5 percent; as a result, these characteristics did not help to explain the variation in skew in the study area. Therefore, a constant model having a regional skew coefficient of 0.086 and an average variance of prediction (AVPnew) (which corresponds to the mean square error [MSE]) of 0.13 at a new streamgage was selected. The AVPnew corresponds to an effective record length of 54 years, a marked improvement over the Bulletin 17B national skew map, whose reported MSE of 0.302 indicated a corresponding effective record length of only 17 years.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195105","usgsCitation":"Veilleux, A.G., and Wagner, D.M., 2019, Methods for estimating regional skewness of annual peak flows in parts of the Great Lakes and Ohio River Basins, based on data through water year 2013: U.S. Geological Survey Scientific Investigations Report 2019–5105, 26 p., https://doi.org/10.3133/sir20195105.","productDescription":"Report: vi, 25 p.; 5 Figures; Table; Data Release","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-101994","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":371689,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2019/5105/sir20195105_table1.xlsx","text":"Table 1","size":"99.5 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Streamgages in parts of the Great Lakes and Ohio River Basins considered for use in regional skew analysis"},{"id":371684,"rank":5,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2019/5105/sir20195105_fig01a.pdf","text":"Figure 1A","size":"5.25 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Map of study area in the Great Lakes and Ohio River Basins showing 4-digit hydrologic units"},{"id":371685,"rank":6,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2019/5105/sir20195105_fig01b.pdf","text":"Figure 1B","size":"2.41 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Map of study area in the Great Lakes and Ohio River Basins showing locations of streamgages used in skew analysis"},{"id":371682,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9N7UAFJ","text":"USGS data release","linkHelpText":"Annual peak-flow data, PeakFQ specification files and PeakFQ output files for 368 selected streamflow gaging stations operated by the U.S. Geological Survey in the Great 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The U.S. Geological Survey National Crustal Model (NCM) is being developed to assist with earthquake hazard and risk assessment by supporting estimates of ground shaking in response to an earthquake. The period-dependent intensity and duration of shaking depend upon the three-dimensional seismic velocity, seismic attenuation, and density distribution of a region, which in turn is governed to a large degree by geology and how that geology behaves under varying temperatures and pressures.
A three-dimensional temperature model is presented here to support the estimation of physical parameters within the U.S. Geological Survey NCM. The crustal model is defined by a geological framework consisting of various lithologies with distinct mineral compositions. A temperature model is needed to calculate mineral density and bulk and shear modulus as a function of position within the crust. These properties control seismic velocity and impedance, which are needed to accurately estimate earthquake travel times and seismic amplitudes in earthquake hazard analyses. The temperature model is constrained by observations of surface temperature, temperature gradient, and conductivity, inferred Moho temperature and depth, and assumed conductivity at the base of the crust. The continental plate is assumed to have heat production that decreases exponentially with depth and thermal conductivity that exponentially changes from a surface value to 3.6 watts per meter-Kelvin at the Moho. The oceanic plate cools as a half-space with a geotherm dependent on plate age. Under these conditions, and the application of observed surface heat production, predicted Moho temperatures match Moho temperatures inferred from seismic P-wave velocities, on average. As has been noted in previous studies, high crustal temperatures are found in the western United States, particularly beneath areas of recent volcanism. In the central and eastern United States, elevated temperatures are found from southeast Texas, into the Mississippi Embayment, and up through Wisconsin. A USGS ScienceBase data release that supports this report is available and consists of grids covering the NCM across the conterminous United States, for example, surface temperature and temperature gradient, that are needed to produce temperature profiles.
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U.S. Geological Survey
Box 25046, MS-966
Denver, CO 80225-0046