{"pageNumber":"373","pageRowStart":"9300","pageSize":"25","recordCount":40804,"records":[{"id":70198033,"text":"70198033 - 2018 - Candidate products for operational earthquake forecasting illustrated using the HayWired planning scenario, including one very quick (and not‐so‐dirty) hazard‐map option","interactions":[],"lastModifiedDate":"2020-09-01T14:08:49.812533","indexId":"70198033","displayToPublicDate":"2018-07-09T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Candidate products for operational earthquake forecasting illustrated using the HayWired planning scenario, including one very quick (and not‐so‐dirty) hazard‐map option","docAbstract":"

In an effort to help address debates on the usefulness of operational earthquake forecasting (OEF), we illustrate a number of OEF products that could be automatically generated in near‐real time. To exemplify, we use an M\">M 7.1 mainshock on the Hayward fault, which is very similar to the U.S. Geological Survey (USGS) HayWired earthquake planning scenario. Given that there is always some background level of hazard or risk, we emphasize that probability gains (the ratio of short‐term to long‐term‐average estimates) might be of particular interest to users. We also illustrate how such gains are highly sensitive to forecast duration and latency, with the latter representing how long it takes to generate the forecast and/or to take action. The influence of fault‐based information, which has traditionally been ignored in OEF, is also evaluated using the newly developed the third Uniform California Earthquake Rupture Forecast epidemic‐type aftershock sequence (UCERF3‐ETAS) model. We find that the inclusion of faults only makes a difference for hazard and risk metrics that are dominated by large‐event likelihoods. We also show how the ShakeMap of a mainshock represents a decent estimate of the ground motions that have a 6% chance of being exceeded due to aftershocks in the week that follows. The ultimate value of these types of OEF products can only be determined in the context of specific uses, and because these vary widely, institutions responsible for providing OEF products will depend heavily on user feedback, especially when making resource‐allocation decisions.

","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220170241","usgsCitation":"Field, E., and Milner, K.R., 2018, Candidate products for operational earthquake forecasting illustrated using the HayWired planning scenario, including one very quick (and not‐so‐dirty) hazard‐map option: Seismological Research Letters, v. 89, no. 4, p. 1420-1434, https://doi.org/10.1785/0220170241.","productDescription":"15 p.","startPage":"1420","endPage":"1434","ipdsId":"IP-095951","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":29789,"text":"John Wesley Powell Center for Analysis and Synthesis","active":true,"usgs":true}],"links":[{"id":355563,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"89","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-04-18","publicationStatus":"PW","scienceBaseUri":"5b46e541e4b060350a15d061","contributors":{"authors":[{"text":"Field, Edward H. 0000-0001-8172-7882 field@usgs.gov","orcid":"https://orcid.org/0000-0001-8172-7882","contributorId":1165,"corporation":false,"usgs":true,"family":"Field","given":"Edward H.","email":"field@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":false,"id":739725,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Milner, Kevin R.","contributorId":63494,"corporation":false,"usgs":true,"family":"Milner","given":"Kevin","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":739726,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70198015,"text":"70198015 - 2018 - An updated method for estimating landslide‐event magnitude","interactions":[],"lastModifiedDate":"2018-07-13T14:28:34","indexId":"70198015","displayToPublicDate":"2018-07-06T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1425,"text":"Earth Surface Processes and Landforms","active":true,"publicationSubtype":{"id":10}},"title":"An updated method for estimating landslide‐event magnitude","docAbstract":"

Summary statistics derived from the frequency–area distribution (FAD) of inventories of triggered landslides allows for direct comparison of landslides triggered by one event (e.g. earthquake, rainstorm) with another. Such comparisons are vital to understand links between the landslide‐event and the environmental characteristics of the area affected. This could lead to methods for rapid estimation of landslide‐event magnitude, which in turn could lead to estimates of the total triggered landslide area. Previous studies proposed that the FAD of landslides follows an inverse power‐law, which provides the basis to model the size distribution of landslides and to estimate landslide‐event magnitude (mLS), which quantifies the severity of the event. In this study, we use a much larger collection of earthquake‐induced landslide (EQIL) inventories (n=45) than previous studies to show that size distributions are much more variable than previously assumed. We present an updated model and propose a method for estimating mLS and its uncertainty that better fits the observations and is more reproducible, robust, and consistent than existing methods. We validate our model by computing mLS for all of the inventories in our dataset and comparing that with the total landslide areas of the inventories. We show that our method is able to estimate the total landslide area of the events in this larger inventory dataset more successfully than the existing methods. 

","language":"English","publisher":"Wiley","doi":"10.1002/esp.4359","usgsCitation":"Tanyas, H., Allstadt, K.E., and van Weston, C.J., 2018, An updated method for estimating landslide‐event magnitude: Earth Surface Processes and Landforms, v. 43, no. 9, p. 1836-1847, https://doi.org/10.1002/esp.4359.","productDescription":"12 p.","startPage":"1836","endPage":"1847","ipdsId":"IP-090008","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":468601,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/esp.4359","text":"Publisher Index Page"},{"id":437830,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F79022QD","text":"USGS data release","linkHelpText":"landslides-mLS"},{"id":355523,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"43","issue":"9","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-14","publicationStatus":"PW","scienceBaseUri":"5b46e541e4b060350a15d067","contributors":{"authors":[{"text":"Tanyas, Hakan","contributorId":198731,"corporation":false,"usgs":false,"family":"Tanyas","given":"Hakan","affiliations":[],"preferred":false,"id":739604,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Allstadt, Kate E. 0000-0003-4977-5248 kallstadt@usgs.gov","orcid":"https://orcid.org/0000-0003-4977-5248","contributorId":167684,"corporation":false,"usgs":true,"family":"Allstadt","given":"Kate","email":"kallstadt@usgs.gov","middleInitial":"E.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":false,"id":739603,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"van Weston, Cees J.","contributorId":206153,"corporation":false,"usgs":false,"family":"van Weston","given":"Cees","email":"","middleInitial":"J.","affiliations":[{"id":37261,"text":"University of Twente, Netherlands","active":true,"usgs":false}],"preferred":false,"id":739605,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70198011,"text":"70198011 - 2018 - Using cluster analysis to compartmentalize a large managed wetland based on physical, biological, and climatic geospatial attributes","interactions":[],"lastModifiedDate":"2018-09-10T10:59:41","indexId":"70198011","displayToPublicDate":"2018-07-06T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1547,"text":"Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Using cluster analysis to compartmentalize a large managed wetland based on physical, biological, and climatic geospatial attributes","docAbstract":"

Hierarchical and partitional cluster analyses were used to compartmentalize Water Conservation Area 1, a managed wetland within the Arthur R. Marshall Loxahatchee National Wildlife Refuge in southeast Florida, USA, based on physical, biological, and climatic geospatial attributes. Single, complete, average, and Ward’s linkages were tested during the hierarchical cluster analyses, with average linkage providing the best results. In general, the partitional method, partitioning around medoids, found clusters that were more evenly sized and more spatially aggregated than those resulting from the hierarchical analyses. However, hierarchical analysis appeared to be better suited to identify outlier regions that were significantly different from other areas. The clusters identified by geospatial attributes were similar to clusters developed for the interior marsh in a separate study using water quality attributes, suggesting that similar factors have influenced variations in both the set of physical, biological, and climatic attributes selected in this study and water quality parameters. However, geospatial data allowed further subdivision of several interior marsh clusters identified from the water quality data, potentially indicating zones with important differences in function. Identification of these zones can be useful to managers and modelers by informing the distribution of monitoring equipment and personnel as well as delineating regions that may respond similarly to future changes in management or climate.

","language":"English","publisher":"Springer","doi":"10.1007/s00267-018-1050-5","usgsCitation":"Hahus, I., Migliaccio, K., Douglas-Mankin, K.R., Klarenberg, G., and Muñoz-Carpena, R., 2018, Using cluster analysis to compartmentalize a large managed wetland based on physical, biological, and climatic geospatial attributes: Environmental Management, v. 62, no. 3, p. 571-583, https://doi.org/10.1007/s00267-018-1050-5.","productDescription":"13 p.","startPage":"571","endPage":"583","ipdsId":"IP-094746","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":355526,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.446,\n 26.356\n ],\n [\n -80.222,\n 26.356\n ],\n [\n -80.222,\n 26.683\n ],\n [\n -80.446,\n 26.683\n ],\n [\n -80.446,\n 26.356\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"62","issue":"3","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2018-04-27","publicationStatus":"PW","scienceBaseUri":"5b46e542e4b060350a15d06b","contributors":{"authors":[{"text":"Hahus, Ian","contributorId":206143,"corporation":false,"usgs":false,"family":"Hahus","given":"Ian","email":"","affiliations":[{"id":37258,"text":"Department of Agricultural and Biological Engineering, University of Florida","active":true,"usgs":false}],"preferred":false,"id":739586,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Migliaccio, Kati","contributorId":111526,"corporation":false,"usgs":true,"family":"Migliaccio","given":"Kati","affiliations":[],"preferred":false,"id":739587,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Douglas-Mankin, Kyle R. 0000-0002-3155-3666","orcid":"https://orcid.org/0000-0002-3155-3666","contributorId":203927,"corporation":false,"usgs":true,"family":"Douglas-Mankin","given":"Kyle","email":"","middleInitial":"R.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":739585,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Klarenberg, Geraldine","contributorId":206145,"corporation":false,"usgs":false,"family":"Klarenberg","given":"Geraldine","email":"","affiliations":[{"id":37258,"text":"Department of Agricultural and Biological Engineering, University of Florida","active":true,"usgs":false}],"preferred":false,"id":739588,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Muñoz-Carpena, Rafael","contributorId":206146,"corporation":false,"usgs":false,"family":"Muñoz-Carpena","given":"Rafael","affiliations":[{"id":37258,"text":"Department of Agricultural and Biological Engineering, University of Florida","active":true,"usgs":false}],"preferred":false,"id":739589,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70198084,"text":"70198084 - 2018 - Modeling the distributions of tegu lizards in native and potential invasive ranges","interactions":[],"lastModifiedDate":"2018-07-13T10:13:21","indexId":"70198084","displayToPublicDate":"2018-07-05T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Modeling the distributions of tegu lizards in native and potential invasive ranges","docAbstract":"

Invasive reptilian predators can have substantial impacts on native species and ecosystems. Tegu lizards are widely distributed in South America east of the Andes, and are popular in the international live animal trade. Two species are established in Florida (U.S.A.) - Salvator merianae (Argentine black and white tegu) and Tupinambis teguixin sensu lato (gold tegu) – and a third has been recorded there— S. rufescens (red tegu). We built species distribution models (SDMs) using 5 approaches (logistic regression, multivariate adaptive regression splines, boosted regression trees, random forest, and maximum entropy) based on data from the native ranges. We then projected these models to North America to develop hypotheses for potential tegu distributions. Our results suggest that much of the southern United States and northern México probably contains suitable habitat for one or more of these tegu species. Salvator rufescens had higher habitat suitability in semi-arid areas, whereas S. merianae and T. teguixin had higher habitat suitability in more mesic areas. We propose that Florida is not the only state where these taxa could become established, and that early detection and rapid response programs targeting tegu lizards in potentially suitable habitat elsewhere in North America could help prevent establishment and abate negative impacts on native ecosystems.

","language":"English","publisher":"Springer","doi":"10.1038/s41598-018-28468-w","usgsCitation":"Jarnevich, C.S., Hayes, M., Fitzgerald, L.A., Yackel, A., Falk, B., Collier, M., Bonewell, L., Klug, P., Naretto, S., and Reed, R., 2018, Modeling the distributions of tegu lizards in native and potential invasive ranges: Scientific Reports, v. 8, e10193; 12 p., https://doi.org/10.1038/s41598-018-28468-w.","productDescription":"e10193; 12 p.","ipdsId":"IP-090713","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":468602,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-018-28468-w","text":"Publisher Index Page"},{"id":437831,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JZZE4W","text":"USGS data release","linkHelpText":"Data for modeling tegu lizard distributions in the Americas"},{"id":355667,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"8","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-07-05","publicationStatus":"PW","scienceBaseUri":"5b6fc418e4b0f5d57878e9e1","contributors":{"authors":[{"text":"Jarnevich, Catherine S. 0000-0002-9699-2336 jarnevichc@usgs.gov","orcid":"https://orcid.org/0000-0002-9699-2336","contributorId":3424,"corporation":false,"usgs":true,"family":"Jarnevich","given":"Catherine","email":"jarnevichc@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":739937,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hayes, Mark","contributorId":206268,"corporation":false,"usgs":false,"family":"Hayes","given":"Mark","affiliations":[],"preferred":false,"id":739938,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fitzgerald, Lee A.","contributorId":141035,"corporation":false,"usgs":false,"family":"Fitzgerald","given":"Lee","email":"","middleInitial":"A.","affiliations":[{"id":6747,"text":"Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":739939,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yackel, Amy 0000-0002-7044-8447 yackela@usgs.gov","orcid":"https://orcid.org/0000-0002-7044-8447","contributorId":152310,"corporation":false,"usgs":true,"family":"Yackel","given":"Amy","email":"yackela@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":739940,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Falk, Bryan 0000-0002-9690-5626 bfalk@usgs.gov","orcid":"https://orcid.org/0000-0002-9690-5626","contributorId":150075,"corporation":false,"usgs":true,"family":"Falk","given":"Bryan","email":"bfalk@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":739941,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Collier, Michelle 0000-0001-5715-448X","orcid":"https://orcid.org/0000-0001-5715-448X","contributorId":206269,"corporation":false,"usgs":true,"family":"Collier","given":"Michelle","email":"","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":739942,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bonewell, Lea","contributorId":206270,"corporation":false,"usgs":true,"family":"Bonewell","given":"Lea","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":739943,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Klug, Page 0000-0002-0836-3901","orcid":"https://orcid.org/0000-0002-0836-3901","contributorId":206271,"corporation":false,"usgs":false,"family":"Klug","given":"Page","affiliations":[{"id":37295,"text":"USDA APHIS","active":true,"usgs":false}],"preferred":false,"id":739944,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Naretto, Sergio","contributorId":206272,"corporation":false,"usgs":false,"family":"Naretto","given":"Sergio","email":"","affiliations":[{"id":37296,"text":"Instituto de Diversidad y Ecología Animal","active":true,"usgs":false}],"preferred":false,"id":739945,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Reed, Robert 0000-0001-8349-6168 reedr@usgs.gov","orcid":"https://orcid.org/0000-0001-8349-6168","contributorId":152301,"corporation":false,"usgs":true,"family":"Reed","given":"Robert","email":"reedr@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":739946,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70197997,"text":"70197997 - 2018 - Transient coastal landscapes: Rising sea level threatens salt marshes","interactions":[],"lastModifiedDate":"2018-07-05T10:20:30","indexId":"70197997","displayToPublicDate":"2018-07-05T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Transient coastal landscapes: Rising sea level threatens salt marshes","docAbstract":"

Salt marshes are important coastal environments that provide key ecological services. As sea level rise has accelerated globally, concerns about the ability of salt marshes to survive submergence are increasing. Previous estimates of likely survival of salt marshes were based on ratios of sea level rise to marsh platform accretion. Here we took advantage of an unusual, long-term (1979–2015), spatially detailed comparison of changes in a representative New England salt marsh to provide an empirical estimate of habitat losses based on actual measurements. We show prominent changes in habitat mosaic within the marsh, consistent and coincident with increased submergence and coastal erosion. Model results suggest that at current rates of sea level rise, marsh platform accretion, habitat loss, and with the limitation of the widespread “coastal squeeze”, the entire ecosystem might disappear by the beginning of the next century, a fate that might be likely for many salt marshes elsewhere. Meta-analysis of available data suggests that 40 to 95% of the world's salt marshes will be submerged, depending on whether sea level rise remains at current or reaches anticipated rates for the end of this century.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2018.05.235","usgsCitation":"Valiela, I., Lloret, J., Bowyer, T., Miner, S., Remsen, D.P., Elmstrom, E., Cogswell, C., and Thieler, E.R., 2018, Transient coastal landscapes: Rising sea level threatens salt marshes: Science of the Total Environment, v. 640-641, p. 1148-1156, https://doi.org/10.1016/j.scitotenv.2018.05.235.","productDescription":"9 p.","startPage":"1148","endPage":"1156","ipdsId":"IP-081985","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":468603,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://hdl.handle.net/1912/10488","text":"Publisher Index Page"},{"id":355497,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"640-641","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b46e544e4b060350a15d075","contributors":{"authors":[{"text":"Valiela, Ivan","contributorId":189387,"corporation":false,"usgs":false,"family":"Valiela","given":"Ivan","email":"","affiliations":[],"preferred":false,"id":739535,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lloret, Javier","contributorId":206128,"corporation":false,"usgs":false,"family":"Lloret","given":"Javier","email":"","affiliations":[{"id":37252,"text":"Ecosystems Center, Marine Biological Laboratory, Woods Hole MA US 02543","active":true,"usgs":false}],"preferred":false,"id":739536,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bowyer, Tynan","contributorId":206129,"corporation":false,"usgs":false,"family":"Bowyer","given":"Tynan","email":"","affiliations":[{"id":37252,"text":"Ecosystems Center, Marine Biological Laboratory, Woods Hole MA US 02543","active":true,"usgs":false}],"preferred":false,"id":739537,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Miner, Simon","contributorId":196953,"corporation":false,"usgs":false,"family":"Miner","given":"Simon","affiliations":[],"preferred":false,"id":739538,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Remsen, David P.","contributorId":196868,"corporation":false,"usgs":false,"family":"Remsen","given":"David","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":739539,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Elmstrom, Elizabeth","contributorId":206130,"corporation":false,"usgs":false,"family":"Elmstrom","given":"Elizabeth","email":"","affiliations":[{"id":37252,"text":"Ecosystems Center, Marine Biological Laboratory, Woods Hole MA US 02543","active":true,"usgs":false}],"preferred":false,"id":739540,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cogswell, Charlotte","contributorId":206131,"corporation":false,"usgs":false,"family":"Cogswell","given":"Charlotte","email":"","affiliations":[{"id":37253,"text":"CR Environmental, Inc. 639 Boxberry Hill Road, East Falmouth, MA US 02536","active":true,"usgs":false}],"preferred":false,"id":739541,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Thieler, E. Robert 0000-0003-4311-9717 rthieler@usgs.gov","orcid":"https://orcid.org/0000-0003-4311-9717","contributorId":2488,"corporation":false,"usgs":true,"family":"Thieler","given":"E.","email":"rthieler@usgs.gov","middleInitial":"Robert","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":739534,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70213052,"text":"70213052 - 2018 - Discussion of “Shallow water hydro-sediment-morphodynamic equations for fluvial processes” by Zhixian Cao, Chunchen Xia, Gareth Pender, and Qingquan Liu","interactions":[],"lastModifiedDate":"2020-09-08T16:25:27.151806","indexId":"70213052","displayToPublicDate":"2018-07-04T11:21:29","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2338,"text":"Journal of Hydraulic Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Discussion of “Shallow water hydro-sediment-morphodynamic equations for fluvial processes” by Zhixian Cao, Chunchen Xia, Gareth Pender, and Qingquan Liu","docAbstract":"

The original paper concerns the formulation and use of depth-integrated equations of motion to model the dynamics of shallow flows that entrain or deposit bed material. A recurring theme of the original paper is the authors’ criticism of related theoretical results published by Iverson and Ouyang (2015). This discussion explains why that criticism is misguided.

","language":"English","publisher":"American Society of Civil Engineers","doi":"10.1061/(ASCE)HY.1943-7900.0001519","usgsCitation":"Iverson, R.M., 2018, Discussion of “Shallow water hydro-sediment-morphodynamic equations for fluvial processes” by Zhixian Cao, Chunchen Xia, Gareth Pender, and Qingquan Liu: Journal of Hydraulic Engineering, 07018009, 3 p., https://doi.org/10.1061/(ASCE)HY.1943-7900.0001519.","productDescription":"07018009, 3 p.","ipdsId":"IP-084425","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":378199,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Iverson, Richard M. 0000-0002-7369-3819 riverson@usgs.gov","orcid":"https://orcid.org/0000-0002-7369-3819","contributorId":536,"corporation":false,"usgs":true,"family":"Iverson","given":"Richard","email":"riverson@usgs.gov","middleInitial":"M.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true}],"preferred":true,"id":798081,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70210190,"text":"70210190 - 2018 - Distinguishing Southern Africa precipitation response by strength of El Niño events and implications for decision-making","interactions":[],"lastModifiedDate":"2020-05-20T12:53:17.304468","indexId":"70210190","displayToPublicDate":"2018-07-03T07:46:49","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1562,"text":"Environmental Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Distinguishing Southern Africa precipitation response by strength of El Niño events and implications for decision-making","docAbstract":"

December–February precipitation in southern Africa during recent El Niño events is studied by distinguishing circulation and precipitation responses during strong and moderate-to-weak events. We find that while both strong and moderate-to-weak El Niño events tend to dry southern Africa, the pattern and magnitude of precipitation anomalies in the region are different, with strong El Niño events resulting in rainfall deficits often less than −0.88 standardized units and deficits of only about half that for the moderate-to-weak case. Additionally, the likelihood of southern Africa receiving less than climatologic precipitation is approximately 80% for strong El Niño events compared to just over 60% for moderate-to-weak El Niño. Strong El Niño events are found to substantially disrupt onshore moisture transports from the Indian Ocean and increase geopotential heights within the Angola Low. Since El Niño is the most predictable component of the climate system that influences southern Africa precipitation, the information provided by this assessment of the likelihood of dry conditions can serve to benefit early warning systems.

","language":"English","publisher":"IOP Science","doi":"10.1088/1748-9326/aacc4c","usgsCitation":"Pomposi, C., Funk, C., Shukla, S., and Magadzire, T., 2018, Distinguishing Southern Africa precipitation response by strength of El Niño events and implications for decision-making: Environmental Research Letters, v. 13, no. 7, 074015, 12 p., https://doi.org/10.1088/1748-9326/aacc4c.","productDescription":"074015, 12 p.","ipdsId":"IP-091549","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":468605,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/aacc4c","text":"Publisher Index Page"},{"id":374953,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Southern Africa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 12.6123046875,\n -35.13787911963418\n ],\n [\n 36.2109375,\n -35.13787911963418\n ],\n [\n 36.2109375,\n -22.471954507739213\n ],\n [\n 12.6123046875,\n -22.471954507739213\n ],\n [\n 12.6123046875,\n -35.13787911963418\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"7","noUsgsAuthors":false,"publicationDate":"2018-07-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Pomposi, Catherine","contributorId":195984,"corporation":false,"usgs":false,"family":"Pomposi","given":"Catherine","email":"","affiliations":[],"preferred":false,"id":789483,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Funk, Chris 0000-0002-9254-6718 cfunk@usgs.gov","orcid":"https://orcid.org/0000-0002-9254-6718","contributorId":167070,"corporation":false,"usgs":true,"family":"Funk","given":"Chris","email":"cfunk@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":789484,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shukla, Shraddhanand","contributorId":224784,"corporation":false,"usgs":false,"family":"Shukla","given":"Shraddhanand","affiliations":[{"id":13549,"text":"UC Santa Barbara Climate Hazards Group","active":true,"usgs":false}],"preferred":false,"id":789485,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Magadzire, Tamuka","contributorId":145822,"corporation":false,"usgs":false,"family":"Magadzire","given":"Tamuka","affiliations":[{"id":16236,"text":"UCSB Climate Hazards Group","active":true,"usgs":false}],"preferred":false,"id":789486,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70197980,"text":"fs20183037 - 2018 - Coastal National Elevation Database","interactions":[],"lastModifiedDate":"2018-07-03T12:42:37","indexId":"fs20183037","displayToPublicDate":"2018-07-03T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-3037","title":"Coastal National Elevation Database","docAbstract":"

The Coastal National Elevation Database (CoNED) Applications Project develops enhanced topographic (land elevation) and bathymetric (water depth) datasets that serve as valuable resources for coastal hazards research (Danielson and others, 2016; Thatcher and others, 2016). These datasets are used widely for mapping inundation zones from riverine flood events, hurricanes, and sea-level rise and for other Earth science applications, such as sediment transport, erosion, and storm impact models.

CoNED is a U.S. Geological Survey (USGS) Coastal-Marine Hazards and Resources Program (formerly Coastal and Marine Geology Program) activity centered at the USGS Earth Resources Observation and Science Center and distributed at other USGS Science Centers. As part of the vision for a 3D Nation, the CoNED Project is working collaboratively with the USGS National Geospatial Program, the National Oceanic and Atmospheric Administration, and the U.S. Army Corps of Engineers through the Interagency Working Group on Ocean and Coastal Mapping to build integrated elevation models in the coastal zone by assimilating the land surface topography with littoral zone and continental shelf bathymetry. Several nongovernmental organizations and Federal agencies, including the Department of the Interior Pacific Islands Climate Adaptation Science Center, the National Park Service, the Nature Conservancy, the Louisiana Coastal Protection and Restoration Authority, and numerous academic institutions, partner to make CoNED a success.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20183037","usgsCitation":"Danielson, J.J., Poppenga, S.K., Tyler, D.J., Palaseanu-Lovejoy, M., and Gesch, D.B., 2018, Coastal National Elevation Database: U.S. Geological Survey Fact Sheet 2018–3037, 2 p., https://doi.org/10.3133/2018.","productDescription":"2 p.","onlineOnly":"N","ipdsId":"IP-093676","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":355477,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2018/3037/coverthb.jpg"},{"id":355478,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2018/3037/fs20183037.pdf","text":"Report","size":"617 kB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2018–3037"}],"contact":"

Director, Earth Resources Observation and Science Center  
U.S. Geological Survey
47914 252nd Street 
Sioux Falls, SD 57198

","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2018-07-03","noUsgsAuthors":false,"publicationDate":"2018-07-03","publicationStatus":"PW","scienceBaseUri":"5b46e544e4b060350a15d07b","contributors":{"authors":[{"text":"Danielson, Jeffrey J. 0000-0003-0907-034X daniels@usgs.gov","orcid":"https://orcid.org/0000-0003-0907-034X","contributorId":3996,"corporation":false,"usgs":true,"family":"Danielson","given":"Jeffrey","email":"daniels@usgs.gov","middleInitial":"J.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":739450,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Poppenga, Sandra K. 0000-0002-2846-6836 spoppenga@usgs.gov","orcid":"https://orcid.org/0000-0002-2846-6836","contributorId":3327,"corporation":false,"usgs":true,"family":"Poppenga","given":"Sandra","email":"spoppenga@usgs.gov","middleInitial":"K.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":739451,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tyler, Dean J. 0000-0002-1542-7539 dtyler@usgs.gov","orcid":"https://orcid.org/0000-0002-1542-7539","contributorId":177897,"corporation":false,"usgs":true,"family":"Tyler","given":"Dean","email":"dtyler@usgs.gov","middleInitial":"J.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":739452,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Palaseanu-Lovejoy, Monica 0000-0002-3786-5118 mpal@usgs.gov","orcid":"https://orcid.org/0000-0002-3786-5118","contributorId":3639,"corporation":false,"usgs":true,"family":"Palaseanu-Lovejoy","given":"Monica","email":"mpal@usgs.gov","affiliations":[{"id":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":739453,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gesch, Dean B. 0000-0002-8992-4933 gesch@usgs.gov","orcid":"https://orcid.org/0000-0002-8992-4933","contributorId":2956,"corporation":false,"usgs":true,"family":"Gesch","given":"Dean","email":"gesch@usgs.gov","middleInitial":"B.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":739454,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70196904,"text":"ofr20181081 - 2018 - U.S. Geological Survey Community for Data Integration 2017 Workshop Proceedings","interactions":[],"lastModifiedDate":"2018-10-24T14:43:27","indexId":"ofr20181081","displayToPublicDate":"2018-07-02T15:50:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-1081","title":"U.S. Geological Survey Community for Data Integration 2017 Workshop Proceedings","docAbstract":"

Executive Summary

The U.S. Geological Survey (USGS) Community for Data Integration (CDI) Workshop was held May 16–19, 2017 at the Denver Federal Center. There were 183 in-person attendees and 35 virtual attendees over four days. The theme of the workshop was “Enabling Integrated Science,” with the purpose of bringing together the community to discuss current topics, shared challenges, and steps forward to advance integrated science at the USGS.

The CDI welcomed several keynote speakers, including Bill Werkheiser, USGS Acting Director; Kevin T. Gallagher, USGS Associate Director of the Core Science Systems Mission Area; Bruce Caron, Earth Science Information Partners Community Architect; and Tim Quinn, Chief of the USGS Office of Enterprise Information. Their presentations focused on the importance of collaborative, cross-disciplinary, and open science and the role of the CDI in identifying and supporting new opportunities in these areas for the USGS and its partners.

In addition to the stated theme, the workshop agenda was driven by the needs of the CDI, with topics highlighting current resources and technologies that could help attendees in their daily work. Topical sessions were proposed by CDI members and included subjects such as data citation, information technology architecture, legacy data, real-time data, and many more. Plenary speakers from the community talked about USGS activities in data science, elevation and hydrography data integration, advanced scientific computing solutions, cloud computing, data-management training, and data-sharing agreements. Two panels addressed the role of the CDI in enabling integrated science and examples of CDI-supported projects in action.

Breakout discussions focused on the workshop theme of “Enabling Integrated Science” and covered five topics: Data and Data Integration, Modeling, Computing Capacity, Science Data Integration, and User Needs and Experience. Sessions on each topic identified actions that could bring the USGS and the broader Earth science community closer to the goal of making integrated science commonplace. The breakouts produced recommendations with the broad themes of improving communication and connections across the USGS, reducing duplication and increasing knowledge transfer, increasing training and testbed opportunities to learn and experiment, and creating community-supported standards to enable better integration and interoperability.

The DataBlast poster and live demonstration session showcased 36 projects from around the CDI and included recent CDI-funded projects as well as other USGS and partner initiatives that were related to data and software integration and discovery.

Importantly, the CDI workshop provided a forum for scientists, technologists, data and resource managers, program managers, and others to convene face to face to discuss common methods, interests, challenges, and solutions related to scientific data and technologies. As a result of this rare convergence, new connections were made across disciplines, backgrounds, and geographical locations, seeding future activities and collaborations. Sharing of ideas from all attendees was encouraged through the use of a mobile application to collect real-time questions and feedback from the audience

The primary outcomes of the workshop are the recommendations from the breakout sessions titled “Roadmap Discussions on Enabling Integrated Science” and from the topical sessions detailed in these proceedings. These sessions, as well as the plenary discussions, identified new areas of collaboration and learning that the CDI will facilitate, such as data science, software development, scientific modeling practices, and user needs and experience. The CDI will build on the results of the workshop to guide its future topics, events, and funding opportunities to support an integrated science capacity for the USGS.

 

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181081","usgsCitation":"Hsu, L., Hutchison, V.B., Langseth, M.L., and Wheeler, B., 2018, U.S. Geological Survey Community for Data Integration 2017 Workshop Proceedings: U.S. Geological Survey Open-File Report 2018–1081, 56 p., https://doi.org/10.3133/ofr20181081.","productDescription":"viii, 56 p.","numberOfPages":"68","onlineOnly":"Y","ipdsId":"IP-092748","costCenters":[{"id":37226,"text":"Core Science Analytics, Synthesis, and Libraries","active":true,"usgs":true}],"links":[{"id":355343,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1081/coverthb.jpg"},{"id":355344,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1081/ofr20181081.pdf","text":"Report","size":"5.98 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1081"}],"contact":"

Core Science Analytics, Synthesis, and Library
U.S. Geological Survey
108 National Center
12201 Sunrise Valley Drive,
Reston, VA 20192

","tableOfContents":"","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"publishedDate":"2018-07-02","noUsgsAuthors":false,"publicationDate":"2018-07-02","publicationStatus":"PW","scienceBaseUri":"5b46e545e4b060350a15d07f","contributors":{"authors":[{"text":"Hsu, Leslie 0000-0002-5353-807X lhsu@usgs.gov","orcid":"https://orcid.org/0000-0002-5353-807X","contributorId":191745,"corporation":false,"usgs":true,"family":"Hsu","given":"Leslie","email":"lhsu@usgs.gov","affiliations":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":true,"id":734967,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hutchison, Vivian B. 0000-0001-5301-3698 vhutchison@usgs.gov","orcid":"https://orcid.org/0000-0001-5301-3698","contributorId":5100,"corporation":false,"usgs":true,"family":"Hutchison","given":"Vivian B.","email":"vhutchison@usgs.gov","affiliations":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":false,"id":734968,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Langseth, Madison L. 0000-0002-4472-9106 mlangseth@usgs.gov","orcid":"https://orcid.org/0000-0002-4472-9106","contributorId":147810,"corporation":false,"usgs":true,"family":"Langseth","given":"Madison","email":"mlangseth@usgs.gov","middleInitial":"L.","affiliations":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":false,"id":734969,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wheeler, Benjamin 0000-0001-5875-1163 bwheeler@usgs.gov","orcid":"https://orcid.org/0000-0001-5875-1163","contributorId":5949,"corporation":false,"usgs":true,"family":"Wheeler","given":"Benjamin","email":"bwheeler@usgs.gov","affiliations":[],"preferred":true,"id":734970,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70199039,"text":"70199039 - 2018 - Real-time nowcasting of microbiological water quality at recreational beaches: A wavelet and artificial neural network-based hybrid modeling approach","interactions":[],"lastModifiedDate":"2018-08-29T15:47:30","indexId":"70199039","displayToPublicDate":"2018-07-02T15:47:12","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Real-time nowcasting of microbiological water quality at recreational beaches: A wavelet and artificial neural network-based hybrid modeling approach","docAbstract":"

The number of beach closings caused by bacterial contamination has continued to rise in recent years, putting beachgoers at risk of exposure to contaminated water. Current approaches predict levels of indicator bacteria using regression models containing a number of explanatory variables. Data-based modeling approaches can supplement routine monitoring data and provide highly accurate short-term forecasts of beach water quality. In this paper, we apply the nonlinear autoregressive network with exogenous inputs (NARX) method with explanatory variables to predict Escherichia coli concentrations at four Lake Michigan beach sites. We also apply the nonlinear input–output network (NIO) and nonlinear autoregressive neural network (NAR) methods in addition to a hybrid wavelet-NAR (WA-NAR) model and demonstrate their application. All models were tested using 3 months of observed data. Results revealed that the NARX models provided the best performance and that the WA-NAR model, which requires no explanatory variables, outperformed the NIO and NAR models; therefore, the WA-NAR model is suitable for application to data scarce regions. The models proposed in this paper were evaluated using multiple performance metrics, including sensitivity and specificity measures, and produced results comparable or superior to those of previous mechanistic and statistical models developed for the same beach sites. The relatively high R2 values between data and the NARX models (R2 values of ∼0.8 for the beach sites and ∼0.9 for the river site) indicate that the new class of models shows promise for beach management.

","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.8b01022","usgsCitation":"Zhang, J., Qiu, H., Li, X., Niu, J., Nevers, M., Hu, X., and Phanikumar, M.S., 2018, Real-time nowcasting of microbiological water quality at recreational beaches: A wavelet and artificial neural network-based hybrid modeling approach: Environmental Science & Technology, v. 52, no. 15, p. 8446-8455, https://doi.org/10.1021/acs.est.8b01022.","productDescription":"10 p.","startPage":"8446","endPage":"8455","ipdsId":"IP-094837","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":356935,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"52","issue":"15","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2018-06-29","publicationStatus":"PW","scienceBaseUri":"5b98a2a2e4b0702d0e842f98","contributors":{"authors":[{"text":"Zhang, Juan","contributorId":207432,"corporation":false,"usgs":false,"family":"Zhang","given":"Juan","email":"","affiliations":[{"id":37539,"text":"Jinan University","active":true,"usgs":false}],"preferred":false,"id":743839,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Qiu, Han","contributorId":207433,"corporation":false,"usgs":false,"family":"Qiu","given":"Han","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":743840,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Li, Xiaoyu","contributorId":207434,"corporation":false,"usgs":false,"family":"Li","given":"Xiaoyu","email":"","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":743841,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Niu, Jie","contributorId":207435,"corporation":false,"usgs":false,"family":"Niu","given":"Jie","email":"","affiliations":[{"id":37539,"text":"Jinan University","active":true,"usgs":false}],"preferred":false,"id":743842,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nevers, Meredith B. 0000-0001-6963-6734","orcid":"https://orcid.org/0000-0001-6963-6734","contributorId":201531,"corporation":false,"usgs":true,"family":"Nevers","given":"Meredith B.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":743838,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hu, Xiaonong","contributorId":207436,"corporation":false,"usgs":false,"family":"Hu","given":"Xiaonong","email":"","affiliations":[{"id":37539,"text":"Jinan University","active":true,"usgs":false}],"preferred":false,"id":743843,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Phanikumar, Mantha S.","contributorId":147924,"corporation":false,"usgs":false,"family":"Phanikumar","given":"Mantha","email":"","middleInitial":"S.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":743844,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70198662,"text":"70198662 - 2018 - On the robustness of N‐mixture models","interactions":[],"lastModifiedDate":"2018-08-14T14:01:30","indexId":"70198662","displayToPublicDate":"2018-07-02T14:01:25","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"On the robustness of N‐mixture models","docAbstract":"

N‐mixture models provide an appealing alternative to mark–recapture models, in that they allow for estimation of detection probability and population size from count data, without requiring that individual animals be identified. There is, however, a cost to using the N‐mixture models: inference is very sensitive to the model's assumptions. We consider the effects of three violations of assumptions that might reasonably be expected in practice: double counting, unmodeled variation in population size over time, and unmodeled variation in detection probability over time. These three examples show that small violations of assumptions can lead to large biases in estimation. The violations of assumptions we consider are not only small qualitatively, but are also small in the sense that they are unlikely to be detected using goodness‐of‐fit tests. In cases where reliable estimates of population size are needed, we encourage investigators to allocate resources to acquiring additional data, such as recaptures of marked individuals, for estimation of detection probabilities.

","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.2362","usgsCitation":"Link, W.A., Schofield, M.R., Barker, R.J., and Sauer, J.R., 2018, On the robustness of N‐mixture models: Ecology, v. 99, no. 7, p. 1547-1551, https://doi.org/10.1002/ecy.2362.","productDescription":"5 p.","startPage":"1547","endPage":"1551","ipdsId":"IP-092400","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":356444,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"99","issue":"7","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2018-06-06","publicationStatus":"PW","scienceBaseUri":"5b98a2a2e4b0702d0e842f9a","contributors":{"authors":[{"text":"Link, William A. 0000-0002-9913-0256 wlink@usgs.gov","orcid":"https://orcid.org/0000-0002-9913-0256","contributorId":146920,"corporation":false,"usgs":true,"family":"Link","given":"William","email":"wlink@usgs.gov","middleInitial":"A.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":742385,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schofield, Matthew R.","contributorId":207010,"corporation":false,"usgs":false,"family":"Schofield","given":"Matthew","email":"","middleInitial":"R.","affiliations":[{"id":37428,"text":"Dept of Math/Stat, University of Otago, New Zealand","active":true,"usgs":false}],"preferred":false,"id":742386,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barker, Richard J.","contributorId":207011,"corporation":false,"usgs":false,"family":"Barker","given":"Richard","email":"","middleInitial":"J.","affiliations":[{"id":37428,"text":"Dept of Math/Stat, University of Otago, New Zealand","active":true,"usgs":false}],"preferred":false,"id":742387,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sauer, John R. 0000-0002-4557-3019 jrsauer@usgs.gov","orcid":"https://orcid.org/0000-0002-4557-3019","contributorId":146917,"corporation":false,"usgs":true,"family":"Sauer","given":"John","email":"jrsauer@usgs.gov","middleInitial":"R.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":742388,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70197967,"text":"ofr20181105 - 2018 - Status of selenium in south San Francisco Bay—A basis for modeling potential guidelines to meet National tissue criteria for fish and a proposed wildlife criterion for birds","interactions":[],"lastModifiedDate":"2018-07-02T16:36:31","indexId":"ofr20181105","displayToPublicDate":"2018-07-02T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-1105","title":"Status of selenium in south San Francisco Bay—A basis for modeling potential guidelines to meet National tissue criteria for fish and a proposed wildlife criterion for birds","docAbstract":"
The U.S. Environmental Protection Agency (EPA) proposed Aquatic Life and AquaticDependent Wildlife Criteria for Selenium (Se) in California’s San Francisco Bay and Delta (Bay-Delta) in June 2016. Here we apply the same modeling methodology—Ecosystem-Scale Selenium Modeling— to an assessment of conditions and documentation of food webs of south San Francisco Bay (South Bay) as an exploratory framework in support of site-specific Se criteria development. Long-term datasets contribute to the basis for modeling, especially the 21-year collection of the clam Macoma petalum from a mudflat at the lower end of South Bay (Lower South Bay). As such, this is a working document that may serve as a basis to establish an understanding of the specifics of Se biodynamics within the estuary and reduce uncertainties about how to protect it. This approach brings together the main factors involved in toxicity: likelihood of high exposure, inherent species sensitivity, and the behavioral ecology (for example, demographics and life history) of an organism in terms of susceptibility to a reproductive toxicant. Species sensitivity is represented here by use of the EPA’s current national tissue Se criterion for fish or that proposed to protect the eggs of aquatic birds for the Bay-Delta (U.S. Environmental Protection Agency, 2016a, 2016b, 2016c). This report also strives to bring together findings and field data across a body of literature for South Bay to provide an integrative assessment.

We find an assemblage of site-specific conditions that could affect modeling:
associated urban processes such as discharges from municipal wastewater treatment plants and drainage from mercury (Hg) mining and limestone extraction are sources of Se that characterize the Lower South Bay as the location of interest for Se exposure; • hydrodynamics are lagoon-like (that is, less flushing), which sustains elevated nutrients and phytoplankton blooms; • managed freshwater sources are a major hydrodynamic component; • birds, in addition to fish, are prominent predators in South Bay; • wetland restoration has recently intervened to play a significant role in ecosystem function that may include uptake of both Hg and Se; • the dietary food web of surficial-sediment to M. petalum is important because of the dominance of this clam species and its elevated Se bioaccumulation potential compared to other local food webs; and 2 • maximal Se concentrations may be limited by transitory or annually renewed food webs (for example, migratory shorebirds and decimation of clams from marshes). We also find that the constructed mechanistic model: • spatially connects to the Palo Alto mudflat site because of data availability; • accurately predicts average observed Se concentrations in M. petalum and in predator species of fish and birds; and • is able to bracket a range of potential protective water-column Se concentrations specific to predator species based on the EPA’s national Se criterion for whole-body fish tissue and a proposed site-specific criterion for bird eggs in the Bay-Delta.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181105","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Luoma, S.N., and Presser, T.S., 2018, Status of selenium in south San Francisco Bay—A basis for modeling potential guidelines to meet National tissue criteria for fish and a proposed wildlife criterion for birds: U.S. Geological Survey Open-File Report 2018–1105, 75 p., https://doi.org/10.3133/ofr20181105.","productDescription":"v, 75 p.","numberOfPages":"84","onlineOnly":"Y","ipdsId":"IP-099162","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":355438,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1105/coverthb.jpg"},{"id":355452,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1105/ofr20181105.pdf","text":"Report","size":"5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1105"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.13706970214844,\n 37.40725549559874\n ],\n [\n -121.91322326660156,\n 37.40725549559874\n ],\n [\n -121.91322326660156,\n 37.52225246712464\n ],\n [\n -122.13706970214844,\n 37.52225246712464\n ],\n [\n -122.13706970214844,\n 37.40725549559874\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

National Research Program
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025

","tableOfContents":"","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2018-07-02","noUsgsAuthors":false,"publicationDate":"2018-07-02","publicationStatus":"PW","scienceBaseUri":"5b46e547e4b060350a15d08f","contributors":{"authors":[{"text":"Luoma, Samuel N. 0000-0001-5443-5091","orcid":"https://orcid.org/0000-0001-5443-5091","contributorId":205506,"corporation":false,"usgs":true,"family":"Luoma","given":"Samuel","email":"","middleInitial":"N.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":739414,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Presser, Theresa S. 0000-0001-5643-0147 tpresser@usgs.gov","orcid":"https://orcid.org/0000-0001-5643-0147","contributorId":2467,"corporation":false,"usgs":true,"family":"Presser","given":"Theresa","email":"tpresser@usgs.gov","middleInitial":"S.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":739413,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70197977,"text":"70197977 - 2018 - Projected 21st century coastal flooding in the Southern California Bight. Part 2: Tools for assessing climate change-driven coastal hazards and socio-economic impacts","interactions":[],"lastModifiedDate":"2018-07-02T11:22:21","indexId":"70197977","displayToPublicDate":"2018-07-02T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2380,"text":"Journal of Marine Science and Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Projected 21st century coastal flooding in the Southern California Bight. Part 2: Tools for assessing climate change-driven coastal hazards and socio-economic impacts","docAbstract":"

This paper is the second of two that describes the Coastal Storm Modeling System (CoSMoS) approach for quantifying physical hazards and socio-economic hazard exposure in coastal zones affected by sea-level rise and changing coastal storms. The modelling approach, presented in Part 1, downscales atmospheric global-scale projections to local scale coastal flood impacts by deterministically computing the combined hazards of sea-level rise, waves, storm surges, astronomic tides, fluvial discharges, and changes in shoreline positions. The method is demonstrated through an application to Southern California, United States, where the shoreline is a mix of bluffs, beaches, highly managed coastal communities, and infrastructure of high economic value. Results show that inclusion of 100-year projected coastal storms will increase flooding by 9–350% (an additional average 53.0 ± 16.0 km2) in addition to a 25–500 cm sea-level rise. The greater flooding extents translate to a 55–110% increase in residential impact and a 40–90% increase in building replacement costs. To communicate hazards and ranges in socio-economic exposures to these hazards, a set of tools were collaboratively designed and tested with stakeholders and policy makers; these tools consist of two web-based mapping and analytic applications as well as virtual reality visualizations. To reach a larger audience and enhance usability of the data, outreach and engagement included workshop-style trainings for targeted end-users and innovative applications of the virtual reality visualizations.

","language":"English","publisher":"MDPI","doi":"10.3390/jmse6030076","usgsCitation":"Erikson, L.H., Barnard, P., O'Neill, A., Wood, N.J., Jones, J.M., Finzi Hart, J., Vitousek, S., Limber, P.W., Hayden, M., Fitzgibbon, M., Lovering, J., and Foxgrover, A.C., 2018, Projected 21st century coastal flooding in the Southern California Bight. Part 2: Tools for assessing climate change-driven coastal hazards and socio-economic impacts: Journal of Marine Science and Engineering, v. 6, no. 3, p. 1-19, https://doi.org/10.3390/jmse6030076.","productDescription":"Article 76; 19 p.","startPage":"1","endPage":"19","ipdsId":"IP-098756","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":468609,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/jmse6030076","text":"Publisher Index Page"},{"id":355449,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.860595703125,\n 32.52828936482526\n ],\n [\n -117.037353515625,\n 32.52828936482526\n ],\n [\n -117.037353515625,\n 34.542762387234845\n ],\n [\n -120.860595703125,\n 34.542762387234845\n ],\n [\n -120.860595703125,\n 32.52828936482526\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"6","issue":"3","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-07-02","publicationStatus":"PW","scienceBaseUri":"5b46e546e4b060350a15d089","contributors":{"authors":[{"text":"Erikson, Li H. 0000-0002-8607-7695 lerikson@usgs.gov","orcid":"https://orcid.org/0000-0002-8607-7695","contributorId":149963,"corporation":false,"usgs":true,"family":"Erikson","given":"Li","email":"lerikson@usgs.gov","middleInitial":"H.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":739424,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barnard, Patrick L. 0000-0003-1414-6476 pbarnard@usgs.gov","orcid":"https://orcid.org/0000-0003-1414-6476","contributorId":147147,"corporation":false,"usgs":true,"family":"Barnard","given":"Patrick L.","email":"pbarnard@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":739425,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"O'Neill, Andrea C. 0000-0003-1656-4372 aoneill@usgs.gov","orcid":"https://orcid.org/0000-0003-1656-4372","contributorId":5351,"corporation":false,"usgs":true,"family":"O'Neill","given":"Andrea C.","email":"aoneill@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":739426,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wood, Nathan J. 0000-0002-6060-9729 nwood@usgs.gov","orcid":"https://orcid.org/0000-0002-6060-9729","contributorId":3347,"corporation":false,"usgs":true,"family":"Wood","given":"Nathan","email":"nwood@usgs.gov","middleInitial":"J.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":739427,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jones, Jeanne M. 0000-0001-7549-9270 jmjones@usgs.gov","orcid":"https://orcid.org/0000-0001-7549-9270","contributorId":4676,"corporation":false,"usgs":true,"family":"Jones","given":"Jeanne","email":"jmjones@usgs.gov","middleInitial":"M.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":739428,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Finzi Hart, Juliette 0000-0003-3179-2699","orcid":"https://orcid.org/0000-0003-3179-2699","contributorId":206104,"corporation":false,"usgs":true,"family":"Finzi Hart","given":"Juliette","email":"","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":739429,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Vitousek, Sean","contributorId":190192,"corporation":false,"usgs":false,"family":"Vitousek","given":"Sean","affiliations":[],"preferred":false,"id":739430,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Limber, Patrick W. 0000-0002-8207-3750 plimber@usgs.gov","orcid":"https://orcid.org/0000-0002-8207-3750","contributorId":196794,"corporation":false,"usgs":true,"family":"Limber","given":"Patrick","email":"plimber@usgs.gov","middleInitial":"W.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":739431,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hayden, Maya","contributorId":206106,"corporation":false,"usgs":false,"family":"Hayden","given":"Maya","affiliations":[{"id":37247,"text":"Point Blue Conservation","active":true,"usgs":false}],"preferred":false,"id":739432,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Fitzgibbon, Michael","contributorId":206105,"corporation":false,"usgs":false,"family":"Fitzgibbon","given":"Michael","email":"","affiliations":[{"id":37247,"text":"Point Blue Conservation","active":true,"usgs":false}],"preferred":false,"id":739444,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Lovering, Jessica 0000-0002-0705-9633","orcid":"https://orcid.org/0000-0002-0705-9633","contributorId":204726,"corporation":false,"usgs":true,"family":"Lovering","given":"Jessica","email":"","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":739445,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Foxgrover, Amy C. 0000-0003-0638-5776 afoxgrover@usgs.gov","orcid":"https://orcid.org/0000-0003-0638-5776","contributorId":3261,"corporation":false,"usgs":true,"family":"Foxgrover","given":"Amy","email":"afoxgrover@usgs.gov","middleInitial":"C.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":739446,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70197946,"text":"sir20185089 - 2018 - Water-quality conditions with an emphasis on cyanobacteria and associated toxins and taste-and-odor compounds in the Kansas River, Kansas, July 2012 through September 2016","interactions":[],"lastModifiedDate":"2018-09-25T06:22:34","indexId":"sir20185089","displayToPublicDate":"2018-07-02T00:00:00","publicationYear":"2018","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":"2018-5089","title":"Water-quality conditions with an emphasis on cyanobacteria and associated toxins and taste-and-odor compounds in the Kansas River, Kansas, July 2012 through September 2016","docAbstract":"

Cyanobacteria cause a multitude of water-quality concerns, including the potential to produce toxins and taste-and-odor compounds that may cause substantial economic and public health concerns, and are of particular interest in lakes, reservoirs, and rivers that are used for drinking-water supply. Extensive cyanobacterial blooms typically do not develop in the Kansas River; however, reservoirs in the lower Kansas River Basin occasionally develop blooms that may affect downstream water quality. During July 2012 through September 2016, continuous and (or) discrete water-quality data were collected at several sites (Wamego, De Soto, and three main reservoir-fed tributaries) on the Kansas River to characterize the sources, frequency and magnitude of occurrence, and causes of cyanobacteria, cyanobacterial toxins, and taste-and-odor compounds and to develop a real-time notification system of changing water-quality conditions that may affect drinking-water treatment.

Algal biomass, as estimated by chlorophyll, was consistently higher at the downstream De Soto site than the upstream Wamego site. Higher algal biomass at the De Soto site likely was caused by algal growth during downstream transport without major losses due to grazing by aquatic organisms or other processes. Algal biomass at the Wamego and De Soto sites was negatively correlated with streamflow and total and bioavailable nutrient concentrations. The negative association between algal biomass and nutrients in the Kansas River likely reflects the relatively strong positive association between nutrient concentrations and streamflows.

Cyanobacteria were relatively common in the Kansas River but rarely dominated the algal community. Like overall algal biomass, cyanobacterial abundances typically were higher at the De Soto site than the Wamego site. Cyanobacterial abundances generally peaked in late summer or early fall (July through October), with smaller peaks occasionally observed in spring (April through May). Cyanobacteria in the Kansas River rarely exceeded 20,000 cells per milliliter, the abundance at which cyanobacteria may become a concern for drinking-water treatment. Relations between cyanobacterial abundance and streamflow, turbidity, and nutrients in the Kansas River were similar to those between chlorophyll and total phytoplankton abundance, indicating the same processes that influence overall algal biomass and dynamics also are influencing cyanobacteria.

The cyanotoxin microcystin was detected in about 27 percent of the samples collected from Kansas River tributary and main-stem sites. Cylindrospermopsin was detected in one sample from the De Soto site. Microcystin occurrence and concentration were similar between the Wamego and De Soto sites. Concentrations exceeded the U.S. Environmental Protection Agency health advisory guidance values for finished drinking water of 0.3 (for bottle-fed infants and pre-school children) and 1.6 micrograms per liter (μg/L; for school-age children and adults) in 6 percent or less of samples collected. These guidance values are for finished drinking water and are not directly applicable to observed environmental concentrations but do provide a benchmark for comparison. Microcystin was detected most often and had the highest concentrations during summer. Though seasonal patterns in microcystin occurrence were generally consistent, seasonal maxima varied by an order of magnitude across years.

The taste-and-odor compounds geosmin and 2-methylisoborneol (MIB) were detected in about 78 and 43 percent of samples, respectively, collected across all sites (main stem and tributaries). Geosmin and MIB occurrence and concentration varied considerably between the Wamego and De Soto sites. Geosmin was detected in about 67 percent of Wamego samples and 81 percent of De Soto samples. The human detection threshold of 5 nanograms per liter (ng/L) was exceeded for geosmin in about 11 and 17 percent of the samples collected at the Wamego and De Soto sites, respectively. Geosmin was detected during all months of the year at both sites, and there were no clear seasonal patterns. MIB was detected less frequently in the Kansas River than geosmin and was observed in about 42 percent of Wamego samples and 33 percent of De Soto samples. Concentrations exceeded 5 ng/L in about 7 and 5 percent of samples from the Wamego and De Soto sites, respectively. As observed for geosmin, there were no clear seasonal patterns in MIB occurrence or concentration.

There seems to be a connection between microcystin detections in the Kansas River and occurrence of microcystin in upstream reservoirs (and tributary streams). Microcystin concentrations greater than 0.3 μg/L may be likely during the summer when streamflow is less than 3,000 cubic feet per second (ft3/s) and contributions from Milford Lake exceed about 30 percent of total flow in the Kansas River. Observed microcystin concentrations typically were higher at the De Soto site than the Wamego or tributary sites during 2012 through 2016, indicating cyanobacteria may continue to grow and produce microcystin once introduced to the Kansas River.

The spatial and temporal patterns in geosmin and MIB occurrence and concentration were more complex than microcystin. There were no clear connections between geosmin and MIB occurrence in the Kansas River and potential upstream reservoir (or tributary stream) sources. Likewise, there was not a clear relation between algal biomass, cyanobacteria, or actinomycetes bacteria and taste-and-odor events in the Kansas River. Geosmin and MIB were not strongly correlated with any measured environmental variable at either Kansas River site.

Continuous water-quality data may be used independently or in combination with regression models to provide information on changing water-quality conditions that may affect drinking-water treatment processes or recreational activities on the Kansas River. For example, logistic regression model outputs and continuous water-quality data may both be indicative of the potential for microcystin events. Logistic regression models that are estimating a high probability of microcystin occurrence at concentrations above 0.1 μg/L can be used as one indicator. Streamflows less than 3,000 ft3/s during upstream reservoir releases during periods with low turbidity and increased chlorophyll fluorescence, specific conductance, and pH values may also be indicative of microcystin events. Advanced or near-real-time notification may inform proactive, rather than reactive, management strategies when water-quality conditions are changing rapidly or are likely to cause cyanobacteria-related events.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185089","collaboration":"Prepared in cooperation with the Kansas Water Office, the City of Lawrence, the City of Olathe, the City of Topeka, and Johnson County WaterOne","usgsCitation":"Graham, J.L., Foster, G.M., Williams, T.J., Mahoney, M.D., May, M.R., and Loftin, K.A., 2018, Water-quality conditions with an emphasis on cyanobacteria and associated toxins and taste-and-odor compounds in the Kansas River, Kansas, July 2012 through September 2016: U.S. Geological Survey Scientific Investigations Report 2018–5089, 55 p., https://doi.org/10.3133/sir20185089.","productDescription":"Report: vi, 54 p.; 6 Appendixes; 2 Data Releases","numberOfPages":"66","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-091849","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":355473,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EVITTP","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Phytoplankton data for the Kansas River and tributaries, July 2012 through February 2017"},{"id":355474,"rank":10,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P973V4A9","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Discrete water-quality data for the Kansas River and tributaries, July 2012 - September 2016"},{"id":355471,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2018/5089/sir20185089_appendix5.pdf","text":"Appendix 5","size":"239kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5089 Appendix 5"},{"id":355465,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5089/coverthb2.jpg"},{"id":355472,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2018/5089/sir20185089_appendix6.pdf","text":"Appendix 6","size":"701 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5089 Appendix 6"},{"id":355466,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5089/sir20185089.pdf","text":"Report","size":"3.05 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5089"},{"id":355467,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2018/5089/sir20185089_appendix1.pdf","text":"Appendix 1","size":"365 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5089 Appendix 1"},{"id":355468,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2018/5089/sir20185089_appendix2.pdf","text":"Appendix 2","size":"370 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5089 Appendix 2"},{"id":355469,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2018/5089/sir20185089_appendix3.pdf","text":"Appendix 3","size":"377 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5089 Appendix 3"},{"id":355470,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2018/5089/sir20185089_appendix4.pdf","text":"Appendix 4","size":"372 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5089 Appendix 4"}],"country":"United States","state":"Kansas","otherGeospatial":"Kansas River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97,\n 38.5\n ],\n [\n -94.6307373046875,\n 38.5\n ],\n [\n -94.6307373046875,\n 40\n ],\n [\n -97,\n 40\n ],\n [\n -97,\n 38.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049 

","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2018-07-02","noUsgsAuthors":false,"publicationDate":"2018-07-02","publicationStatus":"PW","scienceBaseUri":"5b46e547e4b060350a15d091","contributors":{"authors":[{"text":"Graham, Jennifer L. 0000-0002-6420-9335 jlgraham@usgs.gov","orcid":"https://orcid.org/0000-0002-6420-9335","contributorId":150737,"corporation":false,"usgs":true,"family":"Graham","given":"Jennifer L.","email":"jlgraham@usgs.gov","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":false,"id":739270,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Foster, Guy M. 0000-0002-9581-057X gfoster@usgs.gov","orcid":"https://orcid.org/0000-0002-9581-057X","contributorId":149145,"corporation":false,"usgs":true,"family":"Foster","given":"Guy","email":"gfoster@usgs.gov","middleInitial":"M.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":739271,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Williams, Thomas J. 0000-0003-3124-3243 tjwilliams@usgs.gov","orcid":"https://orcid.org/0000-0003-3124-3243","contributorId":185244,"corporation":false,"usgs":true,"family":"Williams","given":"Thomas","email":"tjwilliams@usgs.gov","middleInitial":"J.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":739272,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mahoney, Matthew D. 0000-0002-9008-7132","orcid":"https://orcid.org/0000-0002-9008-7132","contributorId":206054,"corporation":false,"usgs":true,"family":"Mahoney","given":"Matthew","email":"","middleInitial":"D.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":739273,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"May, Madison R. 0000-0001-9628-4041 mmay@usgs.gov","orcid":"https://orcid.org/0000-0001-9628-4041","contributorId":167612,"corporation":false,"usgs":true,"family":"May","given":"Madison","email":"mmay@usgs.gov","middleInitial":"R.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":false,"id":739274,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Loftin, Keith A. 0000-0001-5291-876X kloftin@usgs.gov","orcid":"https://orcid.org/0000-0001-5291-876X","contributorId":868,"corporation":false,"usgs":true,"family":"Loftin","given":"Keith","email":"kloftin@usgs.gov","middleInitial":"A.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":739275,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70198048,"text":"70198048 - 2018 - Geochemical characterization and modeling of regional groundwater contributing to the Verde River, Arizona between Mormon Pocket and the USGS Clarkdale gage","interactions":[],"lastModifiedDate":"2018-07-16T10:52:46","indexId":"70198048","displayToPublicDate":"2018-07-02T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Geochemical characterization and modeling of regional groundwater contributing to the Verde River, Arizona between Mormon Pocket and the USGS Clarkdale gage","docAbstract":"We use synoptic surveys of stream discharge, stable isotopes, and dissolved noble gases to identify the source of groundwater discharge to the Verde River in central Arizona. The Verde River more than doubles in discharge in Mormon Pocket over a 1.4 km distance that includes three discrete locations of visible spring input to the river and other diffuse groundwater inputs. A detailed study of the Verde River between Mormon Pocket and the USGS Clarkdale Gage was conducted to better constrain the location of groundwater inputs, the geochemical signature and constrain the source of groundwater input. Discharge, water quality parameters (temperature, pH, specific conductance, and dissolved oxygen), stable isotopes (δ18O and δ2H), noble gases (He, Ne, Ar, Kr and Xe), and radon (222Rn) from river water were collected. Groundwater samples from springs and wells in the area were collected and analyzed for tracers measured in the stream along with some additional analytes (major ions, strontium isotopes (87Sr/86Sr), carbon-14, δ13C, and tritium). Groundwater isotopic signature is consistent with a regional groundwater source. Groundwater springs discharging to the river have a depleted stable isotopic signature indicating recharge source up to 1000 m higher than the discharge location in the Verde River and are significantly fresher than stream water. Spring water has a radiocarbon age of several thousand years and some areas have tritium less than the laboratory reporting level or low concentrations of tritium (1.5 TU). The strontium isotopes indicate groundwater interaction with tertiary volcanic rock and Paleozoic sedimentary rocks. Along the study reach with distance downstream, Verde stream water chemistry shows increased 222Rn, freshening, increased 4He, and isotopic depletion with distance downstream. We estimated total groundwater discharge by inverting a stream transport model against 222Rn and discharge measured in the stream. The salinity, 4He, and stable isotope composition of discharging groundwater was then estimated by fitting modeled values to observed in-stream values. Estimated groundwater inflow to the stream was well within the ranges observed in springs, indicating that the main source of streamflow is deep, regional groundwater. These results show that synoptic surveys of environmental tracers in streams can be used to estimate the isotopic composition and constrain the source of groundwater discharging to streams. Our data provide direct field evidence that deep, regional groundwater discharge can be a significant source of streamflow generation in arid, topographically complex watersheds.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2018.06.078","usgsCitation":"Beisner, K.R., Gardner, W.P., and Hunt, A.G., 2018, Geochemical characterization and modeling of regional groundwater contributing to the Verde River, Arizona between Mormon Pocket and the USGS Clarkdale gage: Journal of Hydrology, v. 564, p. 99-114, https://doi.org/10.1016/j.jhydrol.2018.06.078.","productDescription":"15 p.","startPage":"99","endPage":"114","ipdsId":"IP-093900","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true},{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":355615,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","volume":"564","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b46e545e4b060350a15d083","contributors":{"authors":[{"text":"Beisner, Kimberly R. 0000-0002-2077-6899 kbeisner@usgs.gov","orcid":"https://orcid.org/0000-0002-2077-6899","contributorId":2733,"corporation":false,"usgs":true,"family":"Beisner","given":"Kimberly","email":"kbeisner@usgs.gov","middleInitial":"R.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true},{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":739767,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gardner, W. Payton 0000-0003-0664-001X","orcid":"https://orcid.org/0000-0003-0664-001X","contributorId":206198,"corporation":false,"usgs":false,"family":"Gardner","given":"W.","email":"","middleInitial":"Payton","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":739769,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hunt, Andrew G. 0000-0002-3810-8610 ahunt@usgs.gov","orcid":"https://orcid.org/0000-0002-3810-8610","contributorId":1582,"corporation":false,"usgs":true,"family":"Hunt","given":"Andrew","email":"ahunt@usgs.gov","middleInitial":"G.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":739768,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70198059,"text":"70198059 - 2018 - A semi-arid river in distress: Contributing factors and recovery solutions for three imperiled freshwater mussels (Family Unionidae) endemic to the Rio Grande basin in North America","interactions":[],"lastModifiedDate":"2018-07-12T22:23:57","indexId":"70198059","displayToPublicDate":"2018-07-02T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"A semi-arid river in distress: Contributing factors and recovery solutions for three imperiled freshwater mussels (Family Unionidae) endemic to the Rio Grande basin in North America","docAbstract":"

Freshwater resources in arid and semi-arid regions are in extreme demand, which creates conflicts between needs of humans and aquatic ecosystems. The Rio Grande basin in the southwestern United States and northern Mexico exemplifies this issue, as much of its aquatic biodiversity is in peril as a result of human activities. Unionid mussels have been disproportionately impacted, though the specific factors responsible for their decline remain largely unknown. This is problematic because the Rio Grande basin harbors one federally endangered unionid mussel (Popenaias popeii, Texas Hornshell) plus two other mussel species (Potamilus metnecktayi, Salina Mucket; and Truncilla cognata, Mexican Fawnsfoot), which are also being considered for listing under the U.S. Endangered Species Act. To date, surveys for these species have not corrected for variability in detection so current range estimates may be inaccurate. Using single occupancy-modeling to estimate detection and occupancy at 115 sites along ~800 river kilometers of the Rio Grande in Texas, we found that detection probabilities were relatively high, indicating that our survey design was efficient. In contrast, the estimated occupancy was low, indicating that our focal species were likely rare within the Rio Grande drainage. In general, the predicted occupancy of our focal species was low throughout their respective ranges, indicating possible range declines. A comparison of currently occupied ranges to presumptive ranges underscores this point. The best-approximating models indicated that occupancy was influenced by habitat, water quantity and quality, and proximity to large-scale human activities, such as dams and major urban centers. We also discuss a series of conservation options that may not only improve the long-term prognosis of our focal species but also other aquatic taxa.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2018.03.032","usgsCitation":"Randklev, C.R., Miller, T., Hart, M., Morton, J., Johnson, N.A., Skow, K., Inoue, K., Tsakiris, E., Oetker, S., Smith, R., Robertson, C., and Lopez, R., 2018, A semi-arid river in distress: Contributing factors and recovery solutions for three imperiled freshwater mussels (Family Unionidae) endemic to the Rio Grande basin in North America: Science of the Total Environment, v. 631-632, p. 733-744, https://doi.org/10.1016/j.scitotenv.2018.03.032.","productDescription":"12 p.","startPage":"733","endPage":"744","ipdsId":"IP-091761","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":355630,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Rio Grande basin","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -107.5,34.25 ], [ -107.5,35.75 ], [ -106.0,35.75 ], [ -106.0,34.25 ], [ -107.5,34.25 ] ] ] } } ] }","volume":"631-632","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b46e545e4b060350a15d081","contributors":{"authors":[{"text":"Randklev, Charles R.","contributorId":202530,"corporation":false,"usgs":false,"family":"Randklev","given":"Charles","email":"","middleInitial":"R.","affiliations":[{"id":36313,"text":"Texas A&M","active":true,"usgs":false}],"preferred":false,"id":739813,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miller, Tom","contributorId":206211,"corporation":false,"usgs":false,"family":"Miller","given":"Tom","email":"","affiliations":[{"id":37287,"text":"Laredo Community College","active":true,"usgs":false}],"preferred":false,"id":739814,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hart, Michael","contributorId":206212,"corporation":false,"usgs":false,"family":"Hart","given":"Michael","email":"","affiliations":[{"id":36313,"text":"Texas A&M","active":true,"usgs":false}],"preferred":false,"id":739815,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Morton, Jennifer","contributorId":206213,"corporation":false,"usgs":false,"family":"Morton","given":"Jennifer","email":"","affiliations":[{"id":36313,"text":"Texas A&M","active":true,"usgs":false}],"preferred":false,"id":739816,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Johnson, Nathan A. 0000-0001-5167-1988 najohnson@usgs.gov","orcid":"https://orcid.org/0000-0001-5167-1988","contributorId":4175,"corporation":false,"usgs":true,"family":"Johnson","given":"Nathan","email":"najohnson@usgs.gov","middleInitial":"A.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":739812,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Skow, Kevin","contributorId":206214,"corporation":false,"usgs":false,"family":"Skow","given":"Kevin","email":"","affiliations":[{"id":36313,"text":"Texas A&M","active":true,"usgs":false}],"preferred":false,"id":739817,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Inoue, Kentaro","contributorId":202526,"corporation":false,"usgs":false,"family":"Inoue","given":"Kentaro","email":"","affiliations":[{"id":6747,"text":"Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":739818,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Tsakiris, Eric","contributorId":206215,"corporation":false,"usgs":false,"family":"Tsakiris","given":"Eric","email":"","affiliations":[{"id":36313,"text":"Texas A&M","active":true,"usgs":false}],"preferred":false,"id":739819,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Oetker, Susan","contributorId":206216,"corporation":false,"usgs":false,"family":"Oetker","given":"Susan","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":739820,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Smith, Ryan","contributorId":206257,"corporation":false,"usgs":false,"family":"Smith","given":"Ryan","affiliations":[{"id":7041,"text":"The Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":739911,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Robertson, Clint","contributorId":206217,"corporation":false,"usgs":false,"family":"Robertson","given":"Clint","affiliations":[{"id":37288,"text":"Texas Parks and Wildife","active":true,"usgs":false}],"preferred":false,"id":739821,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Lopez, Roel","contributorId":206218,"corporation":false,"usgs":false,"family":"Lopez","given":"Roel","affiliations":[{"id":36313,"text":"Texas A&M","active":true,"usgs":false}],"preferred":false,"id":739822,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70197974,"text":"70197974 - 2018 - Using geologic structures to constrain constitutive laws not accessible in the laboratory","interactions":[],"lastModifiedDate":"2019-08-15T11:29:27","indexId":"70197974","displayToPublicDate":"2018-07-02T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2468,"text":"Journal of Structural Geology","active":true,"publicationSubtype":{"id":10}},"title":"Using geologic structures to constrain constitutive laws not accessible in the laboratory","docAbstract":"

In this essay, we explore a central problem of structural geology today, and in the foreseeable future, which is the determination of constitutive laws governing rock deformation to produce geologic structures. Although laboratory experiments provide much needed data and insights about constitutive laws, these experiments cannot cover the range of conditions and compositions relevant to the formation of geologic structures. We advocate that structural geologists address this limitation by interpreting natural experiments, documented with field and microstructural data, using continuum mechanical models that enable the deduction of constitutive laws. To put this procedure into a historical context, we review the founding of structural geology by James Hutton in the late 18th century, and the seminal contributions to continuum mechanics from Newton to Cauchy that provide the tools to model geologic structures. The procedure is illustrated with two examples drawn from recent and on-going field investigations of crustal and mantle lithologies. We conclude by pointing to future research opportunities that will engage structural geologists in the pursuit of constitutive laws during the 21st century.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.jsg.2018.06.006","usgsCitation":"Nevitt, J., Warren, J.M., Kumamoto, K.M., and Pollard, D.D., 2018, Using geologic structures to constrain constitutive laws not accessible in the laboratory: Journal of Structural Geology, v. 125, p. 55-63, https://doi.org/10.1016/j.jsg.2018.06.006.","productDescription":"9 p.","startPage":"55","endPage":"63","ipdsId":"IP-094175","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":355443,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"125","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b46e547e4b060350a15d08d","contributors":{"authors":[{"text":"Nevitt, Johanna 0000-0003-3819-1773 jnevitt@usgs.gov","orcid":"https://orcid.org/0000-0003-3819-1773","contributorId":198144,"corporation":false,"usgs":true,"family":"Nevitt","given":"Johanna","email":"jnevitt@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":739409,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Warren, Jessica M. 0000-0002-4046-4200","orcid":"https://orcid.org/0000-0002-4046-4200","contributorId":206098,"corporation":false,"usgs":false,"family":"Warren","given":"Jessica","email":"","middleInitial":"M.","affiliations":[{"id":13359,"text":"University of Delaware","active":true,"usgs":false}],"preferred":false,"id":739410,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kumamoto, Kathryn M.","contributorId":206099,"corporation":false,"usgs":false,"family":"Kumamoto","given":"Kathryn","email":"","middleInitial":"M.","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":739411,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pollard, David D.","contributorId":206100,"corporation":false,"usgs":false,"family":"Pollard","given":"David","email":"","middleInitial":"D.","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":739412,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70197236,"text":"sir20185070 - 2018 - Characterization of peak streamflows and flood inundation of selected areas in southeastern Texas and southwestern Louisiana from the August and September 2017 flood resulting from Hurricane Harvey","interactions":[],"lastModifiedDate":"2018-07-13T09:35:54","indexId":"sir20185070","displayToPublicDate":"2018-07-02T00:00:00","publicationYear":"2018","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":"2018-5070","title":"Characterization of peak streamflows and flood inundation of selected areas in southeastern Texas and southwestern Louisiana from the August and September 2017 flood resulting from Hurricane Harvey","docAbstract":"

Hurricane Harvey made landfall near Rockport, Texas, on August 25, 2017, as a Category 4 hurricane with wind gusts exceeding 150 miles per hour. As Harvey moved inland, the forward motion of the storm slowed down and produced tremendous rainfall amounts over southeastern Texas, with 8-day rainfall amounts exceeding 60 inches in some locations, which is about 15 inches more than average annual amounts of rainfall for eastern Texas and the Texas coast. Historic flooding occurred in Texas as a result of the widespread, heavy rainfall; wind and flood damages were estimated to be $125 billion, and the storm resulted in at least 68 direct fatalities.

In the immediate aftermath of the Harvey-related flood event, the U.S. Geological Survey (USGS) and the Federal Emergency Management Agency initiated a cooperative study to evaluate the magnitude of the flood, determine the probability of occurrence, and map the extent of the flood in Texas. Seventy-four USGS streamflow-gaging stations in Texas with at least 15 years of record and no large data gaps in the period of record had a 2017 annual peak streamflow related to Harvey ranking in the top five of all annual peaks for each given station. New peaks of record streamflow were recorded at 40 of the 74 USGS streamflow-gaging stations. The number of years of peak streamflow record for the 74 analyzed streamflow-gaging stations ranged from 18 to 105, with a mean number of 55 years. The annual exceedance probability estimates for the analyzed streamflow-gaging stations ranged from less than 0.2 to 14.0 percent. USGS field crews surveyed 2,123 high-water marks to obtain water-surface elevations, in feet above the North American Vertical Datum of 1988. In some locations, several water-surface elevations were averaged to obtain 1 water-surface elevation, resulting in 1,258 water-surface elevations. Some of these high-water marks were used, along with peak-stage data from USGS streamflow-gaging stations, to create 19 inundation maps to document the areal extent of the maximum depth of the flooding. Digital datasets of the inundation area, modeling boundary, water-depth rasters, and final map products are available from the USGS data release associated with this report (https://doi.org/10.5066/F7VH5N3N).

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185070","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency","usgsCitation":"Watson, K.M., Harwell, G.R., Wallace, D.S., Welborn, T.L., Stengel, V.G., and McDowell, J.S., 2018, Characterization of peak streamflows and flood inundation of selected areas in southeastern Texas and southwestern Louisiana from the August and September 2017 flood resulting from Hurricane Harvey: U.S. Geological Survey Scientific Investigations Report 2018–5070, 44 p., https://doi.org/10.3133/sir20185070.","productDescription":"Report: viii, 44 p.; Data Release","numberOfPages":"56","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-095268","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":355276,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5070/sir20185070.pdf","text":"Report","size":"12.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5070"},{"id":355275,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5070/coverthb.jpg"},{"id":355277,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7VH5N3N","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data used to characterize peak streamflows and flood inundation resulting from Hurricane Harvey of selected areas in southeastern Texas and southwestern Louisiana, August–September 2017"}],"country":"United States","state":"Arkansas, Louisiana, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -101.00830078125,\n 27.332735136859146\n ],\n [\n -92.7685546875,\n 27.332735136859146\n ],\n [\n -92.7685546875,\n 33.358061612778876\n ],\n [\n -101.00830078125,\n 33.358061612778876\n ],\n [\n -101.00830078125,\n 27.332735136859146\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501

","tableOfContents":"","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"publishedDate":"2018-07-02","noUsgsAuthors":false,"publicationDate":"2018-07-02","publicationStatus":"PW","scienceBaseUri":"5b46e547e4b060350a15d097","contributors":{"authors":[{"text":"Watson, Kara M. 0000-0002-2685-0260 kmwatson@usgs.gov","orcid":"https://orcid.org/0000-0002-2685-0260","contributorId":2134,"corporation":false,"usgs":true,"family":"Watson","given":"Kara","email":"kmwatson@usgs.gov","middleInitial":"M.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":736324,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harwell, Glenn R. 0000-0003-4265-2296","orcid":"https://orcid.org/0000-0003-4265-2296","contributorId":205197,"corporation":false,"usgs":true,"family":"Harwell","given":"Glenn","email":"","middleInitial":"R.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":736325,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wallace, David S. 0000-0002-9134-8197","orcid":"https://orcid.org/0000-0002-9134-8197","contributorId":205198,"corporation":false,"usgs":true,"family":"Wallace","given":"David S.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":736326,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Welborn, Toby L. 0000-0003-4839-2405 tlwelbor@usgs.gov","orcid":"https://orcid.org/0000-0003-4839-2405","contributorId":2295,"corporation":false,"usgs":true,"family":"Welborn","given":"Toby","email":"tlwelbor@usgs.gov","middleInitial":"L.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":736327,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stengel, Victoria G. 0000-0003-0481-3159 vstengel@usgs.gov","orcid":"https://orcid.org/0000-0003-0481-3159","contributorId":5932,"corporation":false,"usgs":true,"family":"Stengel","given":"Victoria","email":"vstengel@usgs.gov","middleInitial":"G.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":736328,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McDowell, Jeremy S. 0000-0002-8132-9806","orcid":"https://orcid.org/0000-0002-8132-9806","contributorId":205199,"corporation":false,"usgs":true,"family":"McDowell","given":"Jeremy S.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":736329,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70202687,"text":"70202687 - 2018 - Metamodeling for groundwater age forecasting in the Lake Michigan Basin","interactions":[],"lastModifiedDate":"2019-03-18T16:30:22","indexId":"70202687","displayToPublicDate":"2018-07-01T16:30:14","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Metamodeling for groundwater age forecasting in the Lake Michigan Basin","docAbstract":"

Groundwater age is an important indicator of groundwater susceptibility to anthropogenic contamination and a key input to statistical models for forecasting water quality. Numerical models can provide estimates of groundwater age, enabling interpretation of measured age tracers. However, to extend to national‐scale groundwater systems where numerical models are not routinely available, a more efficient metamodeling approach can provide a less precise but widely applicable estimate of groundwater age, trained to make forecasts based on predictor variables that can be measured independent of numerical models. We trained gradient‐boosted regression tree statistical metamodels to MODFLOW/MODPATH‐derived groundwater age estimates in five inset models in the Lake Michigan Basin, USA. Using high‐throughput computing, we explored an exhaustive range of tuning parameters and tested metamodels through cross validation, a 20% holdout, and a round robin approach among the five inset models withholding each inset model from training and testing on the held‐out inset model. Forecast skill—measured by Nash Sutcliffe efficiency—was high for age‐related responses in the 20% hold‐out case (ranging from 0.73 to 0.84). The round robin analysis provided the opportunity to explore extending to unmodeled areas and a greater range of skill indicated the need to evaluate when it is appropriate to apply a metamodel from one region to another. We further explored the ramifications of metamodel simplification achieved through removing predictor variables based on their estimated importance. We found that similar metamodel performance was achievable with a fraction of the candidate set of predictor variables with well construction variables being most important.

","language":"English","publisher":"AGU","doi":"10.1029/2017WR022387","usgsCitation":"Fienen, M.N., Nolan, B.T., Kauffman, L.J., and Feinstein, D.T., 2018, Metamodeling for groundwater age forecasting in the Lake Michigan Basin: Water Resources Research, v. 54, no. 7, p. 4750-4766, https://doi.org/10.1029/2017WR022387.","productDescription":"17 p.","startPage":"4750","endPage":"4766","ipdsId":"IP-096251","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":468611,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2017wr022387","text":"Publisher Index Page"},{"id":437832,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7610ZMG","text":"USGS data release","linkHelpText":"Data and Scripts for Metamodeling for Groundwater Age Forecasting in the Lake Michigan Basin"},{"id":362158,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Lake Michigan Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89,\n 41.5\n ],\n [\n -84,\n 41.5\n ],\n [\n -84,\n 46.5\n ],\n [\n -89,\n 46.5\n ],\n [\n -89,\n 41.5\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"54","issue":"7","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2018-07-14","publicationStatus":"PW","contributors":{"authors":[{"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":759477,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nolan, B. Thomas 0000-0002-6945-9659","orcid":"https://orcid.org/0000-0002-6945-9659","contributorId":8905,"corporation":false,"usgs":true,"family":"Nolan","given":"B.","email":"","middleInitial":"Thomas","affiliations":[],"preferred":true,"id":759478,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kauffman, Leon J. 0000-0003-4564-0362 lkauff@usgs.gov","orcid":"https://orcid.org/0000-0003-4564-0362","contributorId":1094,"corporation":false,"usgs":true,"family":"Kauffman","given":"Leon","email":"lkauff@usgs.gov","middleInitial":"J.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":759479,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Feinstein, Daniel T. 0000-0003-1151-2530 dtfeinst@usgs.gov","orcid":"https://orcid.org/0000-0003-1151-2530","contributorId":1907,"corporation":false,"usgs":true,"family":"Feinstein","given":"Daniel","email":"dtfeinst@usgs.gov","middleInitial":"T.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":759480,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70200910,"text":"70200910 - 2018 - Environmental controls, emergent scaling, and predictions of greenhouse gas (GHG) fluxes in coastal salt marshes","interactions":[],"lastModifiedDate":"2018-11-14T15:03:45","indexId":"70200910","displayToPublicDate":"2018-07-01T15:03:36","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2319,"text":"Journal of Geophysical Research G: Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Environmental controls, emergent scaling, and predictions of greenhouse gas (GHG) fluxes in coastal salt marshes","docAbstract":"

Coastal salt marshes play an important role in mitigating global warming by removing atmospheric carbon at a high rate. We investigated the environmental controls and emergent scaling of major greenhouse gas (GHG) fluxes such as carbon dioxide (CO2) and methane (CH4) in coastal salt marshes by conducting data analytics and empirical modeling. The underlying hypothesis is that the salt marsh GHG fluxes follow emergent scaling relationships with their environmental drivers, leading to parsimonious predictive models. CO2 and CH4 fluxes, photosynthetically active radiation (PAR), air and soil temperatures, well water level, soil moisture, and porewater pH and salinity were measured during May–October 2013 from four marshes in Waquoit Bay and adjacent estuaries, MA, USA. The salt marshes exhibited high CO2 uptake and low CH4 emission, which did not significantly vary with the nitrogen loading gradient (5–126 kg · ha−1 · year−1) among the salt marshes. Soil temperature was the strongest driver of both fluxes, representing 2 and 4–5 times higher influence than PAR and salinity, respectively. Well water level, soil moisture, and pH did not have a predictive control on the GHG fluxes, although both fluxes were significantly higher during high tides than low tides. The results were leveraged to develop emergent power law‐based parsimonious scaling models to accurately predict the salt marsh GHG fluxes from PAR, soil temperature, and salinity (Nash‐Sutcliffe Efficiency = 0.80–0.91). The scaling models are available as a user‐friendly Excel spreadsheet named Coastal Wetland GHG Model to explore scenarios of GHG fluxes in tidal marshes under a changing climate and environment.

","language":"English","publisher":"AGU","doi":"10.1029/2018JG004556","usgsCitation":"Abdul-Aziz, O.I., Ishitaq, K.S., Tang, J., Moseman-Valtierra, S., Kroeger, K.D., Gonneea Eagle, M., Mora, J., and Morkeski, K., 2018, Environmental controls, emergent scaling, and predictions of greenhouse gas (GHG) fluxes in coastal salt marshes: Journal of Geophysical Research G: Biogeosciences, v. 123, no. 7, p. 2234-2256, https://doi.org/10.1029/2018JG004556.","productDescription":"23 p.","startPage":"2234","endPage":"2256","ipdsId":"IP-093072","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":468613,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2018jg004556","text":"Publisher Index Page"},{"id":359427,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.5,\n 41.54301946112854\n ],\n [\n -70.5833,\n 41.54301946112854\n ],\n [\n -70.5833,\n 41.5833\n ],\n [\n -70.5,\n 41.5833\n ],\n [\n -70.5,\n 41.54301946112854\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"123","issue":"7","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2018-07-28","publicationStatus":"PW","scienceBaseUri":"5bed4274e4b0b3fc5cf91c90","contributors":{"authors":[{"text":"Abdul-Aziz, Omar I.","contributorId":192386,"corporation":false,"usgs":false,"family":"Abdul-Aziz","given":"Omar","email":"","middleInitial":"I.","affiliations":[],"preferred":false,"id":751228,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ishitaq, Khandker S.","contributorId":210612,"corporation":false,"usgs":false,"family":"Ishitaq","given":"Khandker","email":"","middleInitial":"S.","affiliations":[{"id":38119,"text":"Ecological and Water Resources Engineering Laboratory (EWREL), Department of Civil and Environmental Engineering, West Virginia University, 395 Evansdale Drive, PO Box 6103, Morgantown, WV 26506,","active":true,"usgs":false}],"preferred":false,"id":751229,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tang, Jianwu","contributorId":174890,"corporation":false,"usgs":false,"family":"Tang","given":"Jianwu","email":"","affiliations":[{"id":27818,"text":"The Ecosystems Center, Marine Biological Laboratory. Woods Hole, MA 02543.","active":true,"usgs":false}],"preferred":false,"id":751230,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Moseman-Valtierra, Serena","contributorId":140087,"corporation":false,"usgs":false,"family":"Moseman-Valtierra","given":"Serena","email":"","affiliations":[{"id":6923,"text":"University of Rhode Island, Kingston, RI","active":true,"usgs":false}],"preferred":false,"id":751231,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kroeger, Kevin D. 0000-0002-4272-2349 kkroeger@usgs.gov","orcid":"https://orcid.org/0000-0002-4272-2349","contributorId":1603,"corporation":false,"usgs":true,"family":"Kroeger","given":"Kevin","email":"kkroeger@usgs.gov","middleInitial":"D.","affiliations":[{"id":41100,"text":"Coastal and Marine Hazards and Resources Program","active":true,"usgs":true}],"preferred":true,"id":751232,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gonneea Eagle, Meagan 0000-0001-5072-2755 mgonneea@usgs.gov","orcid":"https://orcid.org/0000-0001-5072-2755","contributorId":174590,"corporation":false,"usgs":true,"family":"Gonneea Eagle","given":"Meagan","email":"mgonneea@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":751233,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mora, Jordan","contributorId":208060,"corporation":false,"usgs":false,"family":"Mora","given":"Jordan","email":"","affiliations":[{"id":37699,"text":"Waquoit Bay National Estuarine Research Reserve, Waquoit, Mass","active":true,"usgs":false}],"preferred":false,"id":751234,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Morkeski, Kate","contributorId":210613,"corporation":false,"usgs":false,"family":"Morkeski","given":"Kate","email":"","affiliations":[{"id":38120,"text":"Marine Chemistry and Geochemistry, Woods Hole Oceanographic Institution, 266 Woods Hole Road, Woods Hole, MA 02543, USA","active":true,"usgs":false}],"preferred":false,"id":751235,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70199948,"text":"70199948 - 2018 - Biogeography of pelagic food webs in the North Pacific","interactions":[],"lastModifiedDate":"2018-10-05T14:39:26","indexId":"70199948","displayToPublicDate":"2018-07-01T14:39:18","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1660,"text":"Fisheries Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Biogeography of pelagic food webs in the North Pacific","docAbstract":"

The tufted puffin (Fratercula cirrhata) is a generalist seabird that breeds throughout the North Pacific and eats more than 75 different prey species. Using puffins as samplers, we characterized the geographic variability in pelagic food webs across the subarctic North Pacific from the composition of ~10,000 tufted puffin meals (~56,000 prey items) collected at 35 colonies in the Gulf of Alaska (GoA) and Aleutian Archipelago. Cluster analysis of diet species composition suggested three distinct forage fish communities: (i) in the northern GoA, multiple age‐classes of coastal and shelf residents such as capelin, sand lance and herring dominated the food web, (ii) in the western GoA to eastern Aleutians, the shelf community was dominated by transient age‐0 walleye pollock, and (iii) in the western Aleutians, shelf‐edge and mesopelagic forage species such as squid, lanternfish, and Atka mackerel were prevalent. Geographic patterns of abundance of capelin and sand lance in tufted puffin diets were corroborated by independent research fisheries and diets of piscivorous fish, indicating that puffin diets reflect the local abundance of forage species, not just selection of favored species. Generalized additive models showed that habitat characteristics predict, in a non‐linear fashion, forage species distribution and abundance across two large marine ecosystems. We conclude that major biogeographic patterns in forage fish distribution follow gradients in key habitat features, and puffin diets reflect those patterns.

","language":"English","publisher":"Wiley","doi":"10.1111/fog.12258","usgsCitation":"Piatt, J.F., Arimitsu, M.L., Sydeman, W.J., Thompson, S.A., Renner, H., Zador, S., Douglas, D., Hatch, S., Kettle, A.B., and Williams, J.C., 2018, Biogeography of pelagic food webs in the North Pacific: Fisheries Oceanography, v. 27, no. 4, p. 366-380, https://doi.org/10.1111/fog.12258.","productDescription":"15 p.","startPage":"366","endPage":"380","ipdsId":"IP-067595","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":468614,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/fog.12258","text":"External Repository"},{"id":358187,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"27","issue":"4","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-12","publicationStatus":"PW","scienceBaseUri":"5bc02fd7e4b0fc368eb5398f","contributors":{"authors":[{"text":"Piatt, John F. 0000-0002-4417-5748 jpiatt@usgs.gov","orcid":"https://orcid.org/0000-0002-4417-5748","contributorId":3025,"corporation":false,"usgs":true,"family":"Piatt","given":"John","email":"jpiatt@usgs.gov","middleInitial":"F.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":747424,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Arimitsu, Mayumi L. 0000-0001-6982-2238 marimitsu@usgs.gov","orcid":"https://orcid.org/0000-0001-6982-2238","contributorId":140501,"corporation":false,"usgs":true,"family":"Arimitsu","given":"Mayumi","email":"marimitsu@usgs.gov","middleInitial":"L.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":747425,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sydeman, William J.","contributorId":208489,"corporation":false,"usgs":false,"family":"Sydeman","given":"William","email":"","middleInitial":"J.","affiliations":[{"id":35859,"text":"Farallon Institute","active":true,"usgs":false}],"preferred":false,"id":747426,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thompson, Sarah Ann","contributorId":198394,"corporation":false,"usgs":false,"family":"Thompson","given":"Sarah","email":"","middleInitial":"Ann","affiliations":[],"preferred":false,"id":747427,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Renner, Heather","contributorId":200807,"corporation":false,"usgs":false,"family":"Renner","given":"Heather","affiliations":[],"preferred":false,"id":747428,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zador, Stephani","contributorId":60992,"corporation":false,"usgs":false,"family":"Zador","given":"Stephani","affiliations":[],"preferred":false,"id":747429,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Douglas, David C. 0000-0003-0186-1104 ddouglas@usgs.gov","orcid":"https://orcid.org/0000-0003-0186-1104","contributorId":150115,"corporation":false,"usgs":true,"family":"Douglas","given":"David C.","email":"ddouglas@usgs.gov","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":747430,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hatch, Scott A.","contributorId":201044,"corporation":false,"usgs":false,"family":"Hatch","given":"Scott A.","affiliations":[],"preferred":false,"id":747431,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kettle, Arthur B.","contributorId":98064,"corporation":false,"usgs":false,"family":"Kettle","given":"Arthur","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":747432,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Williams, Jeffrey C.","contributorId":126882,"corporation":false,"usgs":false,"family":"Williams","given":"Jeffrey","email":"","middleInitial":"C.","affiliations":[{"id":6678,"text":"U.S. Fish and Wildlife Service, Alaska Maritime National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":747433,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70237369,"text":"70237369 - 2018 - Sensitivity of streamflow to climate change in California","interactions":[],"lastModifiedDate":"2022-10-11T19:03:35.957365","indexId":"70237369","displayToPublicDate":"2018-07-01T14:00:30","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1246,"text":"Climate Change","onlineIssn":"1573-1480","printIssn":"0165-0009","active":true,"publicationSubtype":{"id":10}},"title":"Sensitivity of streamflow to climate change in California","docAbstract":"Climate change is rapidly altering the global water cycle, exposing vulnerabilities in both social and environmental systems. However, uncertainty in future climate predictions makes it difficult to design and evaluate strategies for building climate resilience. In regions such as California, characterized by stressed water-supply systems, high natural climate variability, and substantial uncertainty in future precipitation projections, alternative approaches to assessing climate risks may be useful. Here, we develop a hydrologic sensitivity approach to estimate regional streamflow responses to climate change in California. We use statistical models to predict monthly streamflow from physical catchment features and evaluate how flow changes with incremental changes in precipitation and temperature. The results indicate unique regional and monthly flow responses to climate change, with early summer flows (May - July) in interior mountain region having the greatest sensitivity to temperature and winter flow (December - March) in the xeric region having the greatest sensitivity to precipitation. When evaluated over the range of global climate model projections for mid-century (2040-2069), models generally suggest shifts in streamflow regimes towards higher wet season flows and lower dry season flows relative to historical conditions. The sensitivity analysis provides insight into catchment- and regional-scale hydrologic responses in California and complements other approaches for understanding the consequences of climatic change for water and risk management.","language":"English","publisher":"Springer","doi":"10.1007/s10584-018-2244-9","usgsCitation":"Grantham, T.E., Carlisle, D.M., McCabe, G.J., and Howard, J., 2018, Sensitivity of streamflow to climate change in California: Climate Change, v. 149, p. 427-441, https://doi.org/10.1007/s10584-018-2244-9.","productDescription":"15 p.","startPage":"427","endPage":"441","ipdsId":"IP-092091","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":408180,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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W. tgrantham@usgs.gov","contributorId":297482,"corporation":false,"usgs":false,"family":"Grantham","given":"Theodore","email":"tgrantham@usgs.gov","middleInitial":"E. W.","affiliations":[{"id":13243,"text":"University of California Berkeley","active":true,"usgs":false}],"preferred":false,"id":854284,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carlisle, Daren M. 0000-0002-7367-348X dcarlisle@usgs.gov","orcid":"https://orcid.org/0000-0002-7367-348X","contributorId":513,"corporation":false,"usgs":true,"family":"Carlisle","given":"Daren","email":"dcarlisle@usgs.gov","middleInitial":"M.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":854285,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCabe, Gregory J. 0000-0002-9258-2997 gmccabe@usgs.gov","orcid":"https://orcid.org/0000-0002-9258-2997","contributorId":200854,"corporation":false,"usgs":true,"family":"McCabe","given":"Gregory","email":"gmccabe@usgs.gov","middleInitial":"J.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":854286,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Howard, Jeanette K.","contributorId":297483,"corporation":false,"usgs":false,"family":"Howard","given":"Jeanette K.","affiliations":[{"id":27697,"text":"The Nature Conservency","active":true,"usgs":false}],"preferred":false,"id":854287,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70200629,"text":"70200629 - 2018 - Direct observations of hydrologic exchange occurring with less‐mobile porosity and the development of anoxic microzones in sandy lakebed sediments","interactions":[],"lastModifiedDate":"2018-10-25T12:33:42","indexId":"70200629","displayToPublicDate":"2018-07-01T12:33:35","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Direct observations of hydrologic exchange occurring with less‐mobile porosity and the development of anoxic microzones in sandy lakebed sediments","docAbstract":"

Quantifying coupled mobile/less‐mobile porosity dynamics is critical to the prediction of biogeochemical storage, release, and transformation processes in the zone where groundwater and surface water exchange. The recent development of fine‐scale geoelectrical monitoring paired with pore‐water sampling in groundwater systems enables direct characterization of hydrologic exchange between more‐ and less‐mobile porosity during tracer tests. We adapt this technique to sandy interface sediments at a groundwater flow‐through kettle lake. Tracer experiments were conducted within controlled‐head permeameters over a range of specified downward flow conditions over several days. Although the bed was predominantly composed of highly permeable sands and gravels, cobble inclusions created less‐mobile flow zones at the centimeter scale. Less‐mobile porosity fractions, residence times, and rates of exchange were inferred from paired bulk and fluid electrical conductivity data, without the need for inverse model calibration. The conservative solute experiments were paired with 15NO3− and other reactive amendments, revealing anaerobic processes occurring at shallow sediment depths where pore‐water sampling indicated bulk‐oxic conditions. The average less‐mobile porosity residence times as evaluated with the geoelectrical method were on 1‐hr timescales, which appear to be biogeochemically important in the context of creating anoxic microzones within less‐mobile porosity of sandy interface sediments.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2018WR022823","usgsCitation":"Briggs, M.A., Day-Lewis, F.D., Dehkordy, F.M., Hampton, T.B., Zarnetske, J.P., Scruggs, C.R., Singha, K., Harvey, J.W., and Lane, J., 2018, Direct observations of hydrologic exchange occurring with less‐mobile porosity and the development of anoxic microzones in sandy lakebed sediments: Water Resources Research, v. 54, no. 7, p. 4714-4729, https://doi.org/10.1029/2018WR022823.","productDescription":"16 p.","startPage":"4714","endPage":"4729","ipdsId":"IP-097710","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":468615,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2018wr022823","text":"Publisher Index Page"},{"id":358819,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"54","issue":"7","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-07-11","publicationStatus":"PW","scienceBaseUri":"5c10a985e4b034bf6a7e526c","contributors":{"authors":[{"text":"Briggs, Martin A. 0000-0003-3206-4132 mbriggs@usgs.gov","orcid":"https://orcid.org/0000-0003-3206-4132","contributorId":4114,"corporation":false,"usgs":true,"family":"Briggs","given":"Martin","email":"mbriggs@usgs.gov","middleInitial":"A.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true}],"preferred":true,"id":749748,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Day-Lewis, Frederick D. 0000-0003-3526-886X daylewis@usgs.gov","orcid":"https://orcid.org/0000-0003-3526-886X","contributorId":1672,"corporation":false,"usgs":true,"family":"Day-Lewis","given":"Frederick","email":"daylewis@usgs.gov","middleInitial":"D.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true}],"preferred":true,"id":749749,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dehkordy, Farzaneh Mahmood Poor","contributorId":210071,"corporation":false,"usgs":false,"family":"Dehkordy","given":"Farzaneh","email":"","middleInitial":"Mahmood Poor","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":749750,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hampton, Tyler B.","contributorId":210072,"corporation":false,"usgs":false,"family":"Hampton","given":"Tyler","email":"","middleInitial":"B.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":749751,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zarnetske, Jay P.","contributorId":210073,"corporation":false,"usgs":false,"family":"Zarnetske","given":"Jay","email":"","middleInitial":"P.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":749752,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Scruggs, Courtney R. 0000-0002-1744-3233 cscruggs@usgs.gov","orcid":"https://orcid.org/0000-0002-1744-3233","contributorId":190406,"corporation":false,"usgs":true,"family":"Scruggs","given":"Courtney","email":"cscruggs@usgs.gov","middleInitial":"R.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":749753,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Singha, Kamini 0000-0002-0605-3774","orcid":"https://orcid.org/0000-0002-0605-3774","contributorId":191366,"corporation":false,"usgs":false,"family":"Singha","given":"Kamini","email":"","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":749754,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Harvey, Judson W. 0000-0002-2654-9873 jwharvey@usgs.gov","orcid":"https://orcid.org/0000-0002-2654-9873","contributorId":1796,"corporation":false,"usgs":true,"family":"Harvey","given":"Judson","email":"jwharvey@usgs.gov","middleInitial":"W.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":749755,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lane, John W. Jr. 0000-0002-3558-243X","orcid":"https://orcid.org/0000-0002-3558-243X","contributorId":210076,"corporation":false,"usgs":true,"family":"Lane","given":"John W.","suffix":"Jr.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true},{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":749756,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70199521,"text":"70199521 - 2018 - Advances in sensitivity analysis of uncertainty to changes in sampling density when modeling spatially correlated attributes","interactions":[],"lastModifiedDate":"2018-09-24T12:24:32","indexId":"70199521","displayToPublicDate":"2018-07-01T12:24:22","publicationYear":"2018","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Advances in sensitivity analysis of uncertainty to changes in sampling density when modeling spatially correlated attributes","docAbstract":"

A comparative analysis of distance methods, kriging and stochastic simulation is conducted for evaluating their capabilities for predicting fluctuations in uncertainty due to changes in spatially correlated samples. It is concluded that distance methods lack the most basic capabilities to assess reliability despite their wide acceptance. In contrast, kriging and stochastic simulation offer significant improvements by considering probabilistic formulations that provide a basis on which uncertainty can be estimated in a way consistent with practices widely accepted in risk analysis. Additionally, using real thickness data of a coal bed, it is confirmed once more that stochastic simulation outperforms kriging.

","language":"English","publisher":"Springer","doi":"10.1007/978-3-319-78999-6_19","usgsCitation":"Olea, R., 2018, Advances in sensitivity analysis of uncertainty to changes in sampling density when modeling spatially correlated attributes, p. 375-393, https://doi.org/10.1007/978-3-319-78999-6_19.","productDescription":"19 p.","startPage":"375","endPage":"393","ipdsId":"IP-081861","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":460784,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/978-3-319-78999-6_19","text":"Publisher Index Page"},{"id":357677,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-06-26","publicationStatus":"PW","scienceBaseUri":"5bc02fd8e4b0fc368eb53991","contributors":{"authors":[{"text":"Olea, Ricardo A. 0000-0003-4308-0808","orcid":"https://orcid.org/0000-0003-4308-0808","contributorId":26436,"corporation":false,"usgs":true,"family":"Olea","given":"Ricardo A.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":745751,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70212616,"text":"70212616 - 2018 - Spatial spectroscopic models for remote exploration","interactions":[],"lastModifiedDate":"2020-08-24T14:34:41.559973","indexId":"70212616","displayToPublicDate":"2018-07-01T09:30:04","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":912,"text":"Astrobiology","active":true,"publicationSubtype":{"id":10}},"title":"Spatial spectroscopic models for remote exploration","docAbstract":"

Ancient hydrothermal systems are a high-priority target for a future Mars sample return mission because they contain energy sources for microbes and can preserve organic materials (Farmer, 2000; MEPAG Next Decade Science Analysis Group, 2008; McLennan et al.,2012; Michalski et al.,2017). Characterizing these large, heterogeneous systems with a remote explorer is difficult due to communications bandwidth and latency; such a mission will require significant advances in spacecraft autonomy. Science autonomy uses intelligent sensor platforms that analyze data in real-time, setting measurement and downlink priorities to provide the best information toward investigation goals. Such automation must relate abstract science hypotheses to the measurable quantities available to the robot. This study captures these relationships by formalizing traditional “science traceability matrices” into probabilistic models. This permits experimental design techniques to optimize future measurements and maximize information value toward the investigation objectives, directing remote explorers that respond appropriately to new data. Such models are a rich new language for commanding informed robotic decision making in physically grounded terms. We apply these models to quantify the information content of different rover traverses providing profiling spectroscopy of Cuprite Hills, Nevada. We also develop two methods of representing spatial correlations using human-defined maps and remote sensing data. Model unit classifications are broadly consistent with prior maps of the site's alteration mineralogy, indicating that the model has successfully represented critical spatial and mineralogical relationships at Cuprite. Key Words: Autonomous science—Imaging spectroscopy—Alteration mineralogy—Field geology—Cuprite—AVIRIS-NG—Robotic exploration. Astrobiology 18, 934–954.

","language":"English","publisher":"Mary Ann Liebert, Inc.","doi":"10.1089/ast.2017.1782","usgsCitation":"Thompson, D.R., Candela, A., Wettergreen, D., Dobrea, E.N., Swayze, G.A., Clark, R.N., and Greenberger, R., 2018, Spatial spectroscopic models for remote exploration: Astrobiology, v. 18, no. 7, p. 934-954, https://doi.org/10.1089/ast.2017.1782.","productDescription":"21 p.","startPage":"934","endPage":"954","ipdsId":"IP-091257","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":377791,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Mars","volume":"18","issue":"7","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Thompson, David R. 0000-0003-0635-5876","orcid":"https://orcid.org/0000-0003-0635-5876","contributorId":225042,"corporation":false,"usgs":false,"family":"Thompson","given":"David","email":"","middleInitial":"R.","affiliations":[{"id":41027,"text":"NASA JPL/CalTech","active":true,"usgs":false}],"preferred":false,"id":797104,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Candela, Alberto","contributorId":225045,"corporation":false,"usgs":false,"family":"Candela","given":"Alberto","email":"","affiliations":[{"id":12943,"text":"Carnegie Mellon University","active":true,"usgs":false}],"preferred":false,"id":797105,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wettergreen, David","contributorId":225057,"corporation":false,"usgs":false,"family":"Wettergreen","given":"David","email":"","affiliations":[{"id":12943,"text":"Carnegie Mellon University","active":true,"usgs":false}],"preferred":false,"id":797106,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dobrea, E. Noe","contributorId":54497,"corporation":false,"usgs":true,"family":"Dobrea","given":"E.","email":"","middleInitial":"Noe","affiliations":[],"preferred":false,"id":797107,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Swayze, Gregg A. 0000-0002-1814-7823 gswayze@usgs.gov","orcid":"https://orcid.org/0000-0002-1814-7823","contributorId":518,"corporation":false,"usgs":true,"family":"Swayze","given":"Gregg","email":"gswayze@usgs.gov","middleInitial":"A.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":797108,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Clark, Roger N","contributorId":115297,"corporation":false,"usgs":true,"family":"Clark","given":"Roger","email":"","middleInitial":"N","affiliations":[],"preferred":false,"id":797109,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Greenberger, Rebecca","contributorId":239535,"corporation":false,"usgs":false,"family":"Greenberger","given":"Rebecca","affiliations":[{"id":7218,"text":"California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":797110,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ]}