The benefits nature provides to people, called ecosystem services, are increasingly recognized and accounted for in assessments of infrastructure development, agricultural management, conservation prioritization, and sustainable sourcing. These assessments are often limited by data, however, a gap with tremendous potential to be filled through Earth observations (EO), which produce a variety of data across spatial and temporal extents and resolutions. Despite widespread recognition of this potential, in practice few ecosystem service studies use EO. Here, we identify challenges and opportunities to using EO in ecosystem service modeling and assessment. Some challenges are technical, related to data awareness, processing, and access. These challenges require systematic investment in model platforms and data management. Other challenges are more conceptual but still systemic; they are byproducts of the structure of existing ecosystem service models and addressing them requires scientific investment in solutions and tools applicable to a wide range of models and approaches. We also highlight new ways in which EO can be leveraged for ecosystem service assessments, identifying promising new areas of research. More widespread use of EO for ecosystem service assessment will only be achieved if all of these types of challenges are addressed. This will require non-traditional funding and partnering opportunities from private and public agencies to promote data exploration, sharing, and archiving. Investing in this integration will be reflected in better and more accurate ecosystem service assessments worldwide.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2019.02.150","usgsCitation":"Ramirez-Reyes, C., Brauman, K.A., Chaplin-Kramer, R., Galford, G.L., Adamo, S.B., Anderson, C.B., Anderson, C., Allington, G.R., Bagstad, K.J., Coe, M.T., Cord, A.F., Dee, L.E., Gould, R.K., Jain, M., Kowal, V.A., Muller-Karger, F.E., Norriss, J., Potapov, P.V., Qui, J., Rieb, J.T., Robinson, B.E., Samberg, L.H., Singh, N., Szeto, S.H., Voigt, B., Watson, K., and Wright, T.M., 2019, Reimagining the potential of Earth observations for ecosystem service assessments: Science of the Total Environment, v. 665, p. 1053-1063, https://doi.org/10.1016/j.scitotenv.2019.02.150.","productDescription":"11 p.","startPage":"1053","endPage":"1063","ipdsId":"IP-103295","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":467876,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2019.02.150","text":"Publisher Index Page"},{"id":361470,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"665","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ramirez-Reyes, Carlos 0000-0002-7407-071X","orcid":"https://orcid.org/0000-0002-7407-071X","contributorId":213446,"corporation":false,"usgs":false,"family":"Ramirez-Reyes","given":"Carlos","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":757742,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brauman, Kate A.","contributorId":190423,"corporation":false,"usgs":false,"family":"Brauman","given":"Kate","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":757743,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chaplin-Kramer, Rebecca 0000-0002-1539-5231","orcid":"https://orcid.org/0000-0002-1539-5231","contributorId":213447,"corporation":false,"usgs":false,"family":"Chaplin-Kramer","given":"Rebecca","email":"","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":757744,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Galford, Gillian L. 0000-0003-2192-7385","orcid":"https://orcid.org/0000-0003-2192-7385","contributorId":213448,"corporation":false,"usgs":false,"family":"Galford","given":"Gillian","email":"","middleInitial":"L.","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":757745,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Adamo, Susana B. 0000-0002-9168-7172","orcid":"https://orcid.org/0000-0002-9168-7172","contributorId":213449,"corporation":false,"usgs":false,"family":"Adamo","given":"Susana","email":"","middleInitial":"B.","affiliations":[{"id":7171,"text":"Columbia University","active":true,"usgs":false}],"preferred":false,"id":757746,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Anderson, Christopher B. 0000-0001-7392-4368","orcid":"https://orcid.org/0000-0001-7392-4368","contributorId":213450,"corporation":false,"usgs":false,"family":"Anderson","given":"Christopher","email":"","middleInitial":"B.","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":757747,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Anderson, Clarissa 0000-0001-5970-0253","orcid":"https://orcid.org/0000-0001-5970-0253","contributorId":213451,"corporation":false,"usgs":false,"family":"Anderson","given":"Clarissa","email":"","affiliations":[{"id":34004,"text":"Scripps Institute of Oceanography","active":true,"usgs":false}],"preferred":false,"id":757748,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Allington, Ginger R. 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Maxwell","contributorId":213467,"corporation":false,"usgs":false,"family":"Wright","given":"T.","email":"","middleInitial":"Maxwell","affiliations":[{"id":16938,"text":"Conservation International","active":true,"usgs":false}],"preferred":false,"id":757767,"contributorType":{"id":1,"text":"Authors"},"rank":27}]}} ,{"id":70205845,"text":"70205845 - 2019 - Factors affecting species richness and distribution spatially and temporally within a protected area using multi-season occupancy models","interactions":[],"lastModifiedDate":"2019-10-08T13:12:54","indexId":"70205845","displayToPublicDate":"2019-02-22T13:10:48","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":774,"text":"Animal Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Factors affecting species richness and distribution spatially and temporally within a protected area using multi-season occupancy models","docAbstract":"Exploring trends in species richness and the distribution of individual species over time as well as the factors affecting these trends informs conservation priorities in protecting species and ecosystems as a whole. We used data from 41 park-wide line transect surveys in 2009 and 2014 and multi-season occupancy models with multi-species data to explore trends in species richness and distribution of individual species and factors affecting these trends in Nyungwe National Park (NNP), Rwanda. Mammalian species richness and the distributional range of 5 of the 7 species increased between 2009 and 2014 in NNP. The probability of colonization of a species into a new area in 2014, where it was not present in 2009, was highest in sites with a lower probability of poaching activity, close to tourist trails, and at lower elevations. The probability of colonization with no poaching activity was about 50% but dropped to about 10% with a 100% chance of poaching activity. Duiker species had the largest increase in distribution during the study, while there was a decrease in the distribution of the eastern chimpanzee and blue monkey. Our results suggest that increased patrols should be implemented in areas of the park with low species richness and areas with a low probability of occurrence for species of conservation concern to combat poaching activity and thus increase the probability of a species moving into a new area. Our use of a single multi-season model for multiple species explicitly accounts for imperfect detection and species-specific identities, while allowing for inferences to be made about rarely detected species by sharing covariates with common species. These results can be used to improve conservation planning in NNP for species management and ranger patrol protocols and our modelling framework is broadly applicable to any protected area with presence/absence species field data.","language":"English","publisher":"Wiley","doi":"10.1111/acv.12491","usgsCitation":"Moore, J.F., Hines, J.E., and Masozera, M.K., 2019, Factors affecting species richness and distribution spatially and temporally within a protected area using multi-season occupancy models: Animal Conservation, v. 22, no. 5, p. 503-514, https://doi.org/10.1111/acv.12491.","productDescription":"12 p.","startPage":"503","endPage":"514","ipdsId":"IP-098950","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":368106,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Rwanda","otherGeospatial":"Nyungwe Forest National Park","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[30.4191,-1.13466],[30.81613,-1.69891],[30.75831,-2.28725],[30.4697,-2.41386],[29.93836,-2.34849],[29.63218,-2.91786],[29.02493,-2.83926],[29.11748,-2.29221],[29.25483,-2.21511],[29.29189,-1.62006],[29.57947,-1.34131],[29.82152,-1.44332],[30.4191,-1.13466]]]},\"properties\":{\"name\":\"Rwanda\"}}]}","volume":"22","issue":"5","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2019-02-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Moore, Jennifer F.","contributorId":189122,"corporation":false,"usgs":false,"family":"Moore","given":"Jennifer","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":772590,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hines, James E. 0000-0001-5478-7230 jhines@usgs.gov","orcid":"https://orcid.org/0000-0001-5478-7230","contributorId":146530,"corporation":false,"usgs":true,"family":"Hines","given":"James","email":"jhines@usgs.gov","middleInitial":"E.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":772589,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Masozera, Michel K.","contributorId":201300,"corporation":false,"usgs":false,"family":"Masozera","given":"Michel","email":"","middleInitial":"K.","affiliations":[{"id":35968,"text":"Wildlife Conservation Society, Rwanda Program","active":true,"usgs":false}],"preferred":false,"id":772591,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70202314,"text":"70202314 - 2019 - Assessing lek attendance of male greater sage‐grouse using fine‐resolution GPS data: Implications for population monitoring of lek mating grouse","interactions":[],"lastModifiedDate":"2021-04-16T19:30:05.818684","indexId":"70202314","displayToPublicDate":"2019-02-21T16:56:50","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3103,"text":"Population Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Assessing lek attendance of male greater sage‐grouse using fine‐resolution GPS data: Implications for population monitoring of lek mating grouse","docAbstract":"Counts of males displaying on breeding grounds are the primary management tool used to assess population trends in lekking grouse species. Despite the importance of male lek attendance (i.e., proportion of males on leks available for detection) influencing lek counts, patterns of within season and between season variability in attendance rates are not well understood. We used high‐frequency global positioning system (GPS) telemetry data from male greater sage‐grouse (Centrocercus urophasianus; n = 67) over five lekking seasons (2013–2017) at eight study sites in Nevada to estimate lek attendance rates. Specifically, we recorded daily locations of sage‐grouse in relation to mapped lek boundaries and used generalized additive models to assess temporal variation in attendance rates by age class (subadult vs. adult). Average timing of peak attendance occurred on 16 April but varied from March 16, 2014 to April 21 , 2016. Overall, adult males attended leks at higher rates (0.683 at peak) and earlier in the season (19 March) than subadults (0.421 at peak on April 19). Peak attendance probability was positively related to cumulative winter precipitation. Daily probabilities of lek switching differed between adults (0.019 at peak on March 3) and subadults (0.046 at peak on March 22), and lek switching was negatively related to distance to nearest lek. Our results indicate variable patterns in lek attendance through time, and that lek switching may occur at higher rates than previously thought. We demonstrate the use of generalizable daily attendance curves to date‐correct lek counts and derive estimates of male abundance, although such an approach will likely require the incorporation of information on age structure to produce robust results that are useful for population monitoring.
","language":"English","publisher":"Wiley","doi":"10.1002/1438-390X.1019","usgsCitation":"Wann, G.T., Coates, P.S., Prochazka, B.G., Severson, J.P., Monroe, A., and Aldridge, C., 2019, Assessing lek attendance of male greater sage‐grouse using fine‐resolution GPS data: Implications for population monitoring of lek mating grouse: Population Ecology, v. 61, no. 2, p. 183-197, https://doi.org/10.1002/1438-390X.1019.","productDescription":"15 p.","startPage":"183","endPage":"197","ipdsId":"IP-098337","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":467880,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/1438-390x.1019","text":"Publisher Index Page"},{"id":361440,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nevada","volume":"61","issue":"2","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2019-02-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Wann, Gregory T. 0000-0001-9076-7819 wanng@usgs.gov","orcid":"https://orcid.org/0000-0001-9076-7819","contributorId":3855,"corporation":false,"usgs":true,"family":"Wann","given":"Gregory","email":"wanng@usgs.gov","middleInitial":"T.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":757781,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coates, Peter S. 0000-0003-2672-9994 pcoates@usgs.gov","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":3263,"corporation":false,"usgs":true,"family":"Coates","given":"Peter","email":"pcoates@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":757780,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Prochazka, Brian G. 0000-0001-7270-5550 bprochazka@usgs.gov","orcid":"https://orcid.org/0000-0001-7270-5550","contributorId":174839,"corporation":false,"usgs":true,"family":"Prochazka","given":"Brian","email":"bprochazka@usgs.gov","middleInitial":"G.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":757782,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Severson, John P. 0000-0002-1754-6689","orcid":"https://orcid.org/0000-0002-1754-6689","contributorId":213469,"corporation":false,"usgs":true,"family":"Severson","given":"John","email":"","middleInitial":"P.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":757783,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Monroe, Adrian P. 0000-0003-0934-8225 amonroe@usgs.gov","orcid":"https://orcid.org/0000-0003-0934-8225","contributorId":152209,"corporation":false,"usgs":true,"family":"Monroe","given":"Adrian P.","email":"amonroe@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":757784,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":213471,"corporation":false,"usgs":false,"family":"Aldridge","given":"Cameron L.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":757785,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70202234,"text":"ofr20171064 - 2019 - Evaluation of recommended revisions to Bulletin 17B","interactions":[],"lastModifiedDate":"2019-02-22T10:08:21","indexId":"ofr20171064","displayToPublicDate":"2019-02-21T15:45:00","publicationYear":"2019","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":"2017-1064","displayTitle":"Evaluation of Recommended Revisions to Bulletin 17B","title":"Evaluation of recommended revisions to Bulletin 17B","docAbstract":"For the past 36 years, Bulletin 17B, published by the Interagency Committee on Water Data in 1982, has guided flood-frequency analyses in the United States. During this period, much has been learned about both hydrology and statistical methods. In keeping with the tradition of periodically updating the Bulletin 17B guidelines in light of advances in our understanding and methods, the Hydrologic Frequency Analysis Work Group (HFAWG) was charged by the Subcommittee on Hydrology (SOH) of the Advisory Committee on Water Information (ACWI) to consider possible updates to Bulletin 17B.
The purpose of this report is to consider the statistical performance of possible revisions to Bulletin 17B procedures. Of particular interest are procedures designed to accommodate more general forms of flood information. The concern is how the proposed procedures would affect the precision, accuracy and robustness of flood-frequency estimates. The investigations reported here focus on techniques for the following:
The proposed changes, which mostly involve generalizing Bulletin 17B’s method-of-moments procedures by using the Expected Moments Algorithm (EMA), are relatively modest, at least in the sense that they would not affect the main features of Bulletin 17B. The proposed methods include the following:
The hydrological literature already provides extensive support for the theory behind the proposed changes. The remaining question is practical: How well do the proposed methods perform under typical and realistic conditions and, specifically, with difficult records occasionally encountered in practice? In order to answer these questions, the HFAWG commissioned the work reported here. The following four major sets of results are provided:
Collectively, these studies provide a reasonably comprehensive, valid, and robust assessment of the properties of the Bulletin 17B methods and proposed alternatives. The experiments and analysis indicate that the flood quantile estimators, proposed as a revision of Bulletin 17B, do the following:
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Reston, VA 20192
Recent information reporting Pink-footed Shearwater Ardenna creatopus mortality from fisheries bycatch throughout its range has encouraged fisheries managers in Chile to evaluate and consider shearwater foraging behaviors to better evaluate risk. In response, we tracked six chickrearing adult Pink-footed Shearwaters from Isla Mocha, off south-central Chile, from 19 to 28 March 2015 using global positioning sensors and time-depth recorders. We recorded seven complete trips averaging 4.2 ± 2.5 d (mean ± SD). Chick-provisioning adults foraged within 334 km (i.e., 175 ± 100 km) of Isla Mocha. Dives (n = 515) occurred throughout the measured foraging range but most frequently occurred within 5–30 km from the mainland coast, in continental shelf waters north of Valdivia. Other regions with diving behavior were within ~20 km of Isla Mocha, and from Lebu to north of Talcahuano. Based on movement behavior analysis, adults spent most of their time at sea “resting/ foraging” (62% ± 6%), with the remainder spent “searching” (16% ± 4%) and “transiting” (20% ± 5%). The proportions of total number of dives associated with these three behaviors were similar. On average, dives were relatively shallow (1.6 ± 1.2 m, maximum depth = 10.1 m) and brief (4.7 ± 4.8 s, maximum duration = 25.7 s). Dives occurred during the day, at night, and at twilight, with most activity occurring at twilight and during the day. Although based on a small sample size, our results may be useful for informing modifications to fishing gear or fisheries policy to reduce the likelihood of bycatch and thus meet Chilean conservation goals for Pink-footed Shearwaters.
","language":"English","publisher":"Pacific Seabird Group","usgsCitation":"Adams, J., Felis, J.J., Czapanskiy, M., Carle, R., and Hodum, P., 2019, Diving behavior of Pink-footed Shearwaters Ardenna creatopus rearing chicks on Isla Mocha, Chile: Marine Ornithology: Journal of Seabird Research and Conservation, v. 47, p. 17-24.","productDescription":"8 p.","startPage":"17","endPage":"24","ipdsId":"IP-092421","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":361421,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":361414,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.marineornithology.org/content/get.cgi?rn=1286"}],"country":"Chile","otherGeospatial":"Isla Mocha","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.5,\n -40\n ],\n [\n -73,\n -40\n ],\n [\n -73,\n -38\n ],\n [\n -74.5,\n -38\n ],\n [\n -74.5,\n -40\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"47","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Adams, Josh 0000-0003-3056-925X","orcid":"https://orcid.org/0000-0003-3056-925X","contributorId":213442,"corporation":false,"usgs":true,"family":"Adams","given":"Josh","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":757734,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Felis, Jonathan J. 0000-0002-0608-8950 jfelis@usgs.gov","orcid":"https://orcid.org/0000-0002-0608-8950","contributorId":4825,"corporation":false,"usgs":true,"family":"Felis","given":"Jonathan","email":"jfelis@usgs.gov","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":757735,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Czapanskiy, Max 0000-0002-6302-905X","orcid":"https://orcid.org/0000-0002-6302-905X","contributorId":207793,"corporation":false,"usgs":false,"family":"Czapanskiy","given":"Max","email":"","affiliations":[{"id":37635,"text":"San Fransciso State University","active":true,"usgs":false}],"preferred":false,"id":757736,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Carle, Ryan D.","contributorId":213443,"corporation":false,"usgs":false,"family":"Carle","given":"Ryan D.","affiliations":[{"id":25597,"text":"Oikonos Ecosystem Knowledge","active":true,"usgs":false}],"preferred":false,"id":757737,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hodum, Peter J.","contributorId":213444,"corporation":false,"usgs":false,"family":"Hodum","given":"Peter J.","affiliations":[{"id":25597,"text":"Oikonos Ecosystem Knowledge","active":true,"usgs":false}],"preferred":false,"id":757738,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70202299,"text":"70202299 - 2019 - Geophysical Characterization of the heat source in the Northwest Geysers, California","interactions":[],"lastModifiedDate":"2019-02-21T13:50:42","indexId":"70202299","displayToPublicDate":"2019-02-21T13:50:37","publicationYear":"2019","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Geophysical Characterization of the heat source in the Northwest Geysers, California","docAbstract":"The Geysers, in northern California, is the largest energy producing geothermal field in the world. Looking to expand capacity, the operator Calpine Corporation developed an anomalously hot (~400 °C at 2.5 km depth) part of the field in the northwest Geysers, including testing of an enhanced geothermal systems (EGS). Though the area is anomalously hot, geophysical methods have failed to adequately image any inferred magmatic heat source. Gravity measurements were collected and jointly modeled with existing magnetic data along a two-dimensional profile aligned with an existing geologic cross-section. The key feature of the potential field model is a low-density, low-susceptibility body below the EGS at 5 km depth. Magnetotelluric (MT) measurements were collected around the northwest Geysers and modeled in three-dimensions to characterize subsurface resistivity structure. The resistivity model images an extension of a Quaternary granitic pluton locally known as “the felsite” under the EGS project and a possible zone of partial melt (<10%) below 7 km in the northwestern part of the field.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings, 44th Workshop on Geothermal Reservoir Engineering","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"44th Workshop on Geothermal Reservoir Engineering","conferenceDate":"February 11-13, 2019","conferenceLocation":"Stanford, CA","language":"English","publisher":"Stanford University","usgsCitation":"Peacock, J., Mangan, M.T., Walters, M., Hartline, C., Glen, J.M., Earney, T.E., and Schermerhorn, W.D., 2019, Geophysical Characterization of the heat source in the Northwest Geysers, California, in Proceedings, 44th Workshop on Geothermal Reservoir Engineering, Stanford, CA, February 11-13, 2019, 7 p.","productDescription":"7 p.","ipdsId":"IP-104721","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":361416,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":361392,"type":{"id":11,"text":"Document"},"url":"https://pangea.stanford.edu/ERE/db/GeoConf/papers/SGW/2019/Peacock.pdf"}],"country":"United States","state":"California","otherGeospatial":"Northwest Geysers","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.9,\n 38.75\n ],\n [\n -122.75,\n 38.75\n ],\n [\n -122.75,\n 38.9\n ],\n [\n -122.9,\n 38.9\n ],\n [\n -122.9,\n 38.75\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Peacock, Jared R. 0000-0002-0439-0224","orcid":"https://orcid.org/0000-0002-0439-0224","contributorId":210082,"corporation":false,"usgs":true,"family":"Peacock","given":"Jared R.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":757698,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mangan, Margaret T. 0000-0002-5273-8053 mmangan@usgs.gov","orcid":"https://orcid.org/0000-0002-5273-8053","contributorId":3343,"corporation":false,"usgs":true,"family":"Mangan","given":"Margaret","email":"mmangan@usgs.gov","middleInitial":"T.","affiliations":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":757699,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walters, Mark 0000-0001-8458-4813","orcid":"https://orcid.org/0000-0001-8458-4813","contributorId":213428,"corporation":false,"usgs":false,"family":"Walters","given":"Mark","email":"","affiliations":[{"id":38755,"text":"Calpine","active":true,"usgs":false}],"preferred":false,"id":757700,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hartline, Craig","contributorId":213429,"corporation":false,"usgs":false,"family":"Hartline","given":"Craig","email":"","affiliations":[{"id":38755,"text":"Calpine","active":true,"usgs":false}],"preferred":false,"id":757701,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Glen, Jonathan M.G. 0000-0002-3502-3355 jglen@usgs.gov","orcid":"https://orcid.org/0000-0002-3502-3355","contributorId":176530,"corporation":false,"usgs":true,"family":"Glen","given":"Jonathan","email":"jglen@usgs.gov","middleInitial":"M.G.","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":757702,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Earney, Tait E. 0000-0002-1504-0457","orcid":"https://orcid.org/0000-0002-1504-0457","contributorId":210080,"corporation":false,"usgs":true,"family":"Earney","given":"Tait","email":"","middleInitial":"E.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":757703,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Schermerhorn, William D. 0000-0002-0167-378X","orcid":"https://orcid.org/0000-0002-0167-378X","contributorId":210081,"corporation":false,"usgs":true,"family":"Schermerhorn","given":"William","email":"","middleInitial":"D.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":757704,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70202304,"text":"70202304 - 2019 - Rupture model of the M5.8 Pawnee, Oklahoma earthquake from regional and teleseismic waveforms","interactions":[],"lastModifiedDate":"2019-06-18T10:18:55","indexId":"70202304","displayToPublicDate":"2019-02-21T13:01:29","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Rupture model of the M5.8 Pawnee, Oklahoma earthquake from regional and teleseismic waveforms","docAbstract":"The 2016 M5.8 Pawnee, Oklahoma earthquake is the largest earthquake to have been induced by wastewater disposal. We infer the coseismic slip history from analysis of apparent source time functions and inversion of regional and teleseismic P‐waveforms, using aftershocks as empirical Green's functions. The earthquake nucleated on the shallow part of the fault, initially rupturing towards the surface, followed shortly thereafter by slip deeper on the fault. Deeper slip occurred below the aftershocks and at greater depths than most induced seismicity in the region, suggesting that small‐ to moderate‐sized earthquakes may not occur on deeper parts of faults in Oklahoma because they are further from failure than shallower fault sections. Comparisons with models of pore pressure perturbations further suggest that the earthquake may have initiated within a region of higher pore pressure perturbation but was not confined to this zone. These observations inform source physics and understanding of maximum magnitudes.
","language":"English","publisher":"AGU","doi":"10.1029/2018GL081364","usgsCitation":"Moschetti, M.P., Hartzell, S.H., and Herrmann, R.B., 2019, Rupture model of the M5.8 Pawnee, Oklahoma earthquake from regional and teleseismic waveforms: Geophysical Research Letters, v. 46, no. 5, p. 2494-2502, https://doi.org/10.1029/2018GL081364.","productDescription":"9 p.","startPage":"2494","endPage":"2502","ipdsId":"IP-104842","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":361410,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oklahoma","city":"Pawnee","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.82697296142577,\n 36.31388025820415\n ],\n [\n -96.77753448486328,\n 36.31388025820415\n ],\n [\n -96.77753448486328,\n 36.35522760439977\n ],\n [\n -96.82697296142577,\n 36.35522760439977\n ],\n [\n -96.82697296142577,\n 36.31388025820415\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"46","issue":"5","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-03-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Moschetti, Morgan P. 0000-0001-7261-0295 mmoschetti@usgs.gov","orcid":"https://orcid.org/0000-0001-7261-0295","contributorId":1662,"corporation":false,"usgs":true,"family":"Moschetti","given":"Morgan","email":"mmoschetti@usgs.gov","middleInitial":"P.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":757716,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hartzell, Stephen H. 0000-0003-0858-9043 shartzell@usgs.gov","orcid":"https://orcid.org/0000-0003-0858-9043","contributorId":2594,"corporation":false,"usgs":true,"family":"Hartzell","given":"Stephen","email":"shartzell@usgs.gov","middleInitial":"H.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":757717,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Herrmann, R. B.","contributorId":213436,"corporation":false,"usgs":false,"family":"Herrmann","given":"R.","email":"","middleInitial":"B.","affiliations":[{"id":37518,"text":"St. Louis University","active":true,"usgs":false}],"preferred":false,"id":757718,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70202301,"text":"70202301 - 2019 - Estimating sand concentrations using ADCP‐based acoustic inversion in a large fluvial system characterized by bi‐modal suspended‐sediment distributions","interactions":[],"lastModifiedDate":"2019-06-18T10:16:40","indexId":"70202301","displayToPublicDate":"2019-02-21T11:11:58","publicationYear":"2019","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":"Estimating sand concentrations using ADCP‐based acoustic inversion in a large fluvial system characterized by bi‐modal suspended‐sediment distributions","docAbstract":"Quantifying sediment flux within rivers is a challenge for many disciplines due, mainly, to difficulties inherent to traditional sediment sampling methods. These methods are operationally complex, high cost, and high risk. Additionally, the resulting data provide a low spatial and temporal resolution estimate of the total sediment flux, which has impeded advances in the understanding of the hydro‐geomorphic characteristics of rivers. Acoustic technologies have been recognized as a leading tool for increasing the resolution of sediment data by relating their echo intensity level measurements to suspended sediment. Further effort is required to robustly test and develop these techniques across a wide range of conditions found in natural river systems. This article aims to evaluate the application of acoustic inversion techniques using commercially available, down‐looking acoustic Doppler current profilers (ADCPs) in quantifying suspended sediment in a large sand bed river with varying bi‐modal particle size distributions, wash load and suspended‐sand ratios, and water stages. To achieve this objective, suspended sediment was physically sampled along the Paraná River, Argentina, under various hydro‐sedimentological regimes. Two ADCPs emitting different sound frequencies were used to simultaneously profile echo intensity level within the water column. Using the sonar equation, calibrations were determined between suspended‐sand concentrations and acoustic backscatter to solve the inverse problem. The study also analyzed the roles played by each term of the sonar equation, such as ADCP frequency, power supply, instrument constants, and particle size distributions typically found in sand bed rivers, on sediment attenuation and backscatter. Calibrations were successfully developed between corrected backscatter and suspended‐sand concentrations for all sites and ADCP frequencies, resulting in mean suspended‐sand concentration estimates within about 40% of the mean sampled concentrations. Noise values, calculated using the sonar equation and sediment sample characteristics, were fairly constant across evaluations, suggesting that they could be applied to other sand bed rivers.
","language":"English","publisher":"Wiley","doi":"10.1002/esp.4572","usgsCitation":"Szupiany, R.N., Lopez Weibel, C., Guerrero, M., Latosinski, F., Wood, M.S., Dominguez Ruben, L., and Oberg, K., 2019, Estimating sand concentrations using ADCP‐based acoustic inversion in a large fluvial system characterized by bi‐modal suspended‐sediment distributions: Earth Surface Processes and Landforms, v. 44, no. 6, p. 1295-1308, https://doi.org/10.1002/esp.4572.","productDescription":"14 p.","startPage":"1295","endPage":"1308","ipdsId":"IP-100807","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":467884,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/esp.4572","text":"External Repository"},{"id":361404,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Argentina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -60.934295654296875,\n -32.11514862261243\n ],\n [\n -60.29296874999999,\n -32.11514862261243\n ],\n [\n -60.29296874999999,\n -31.421631960419596\n ],\n [\n -60.934295654296875,\n -31.421631960419596\n ],\n [\n -60.934295654296875,\n -32.11514862261243\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"44","issue":"6","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2019-01-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Szupiany, Ricardo N.","contributorId":189755,"corporation":false,"usgs":false,"family":"Szupiany","given":"Ricardo","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":757709,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lopez Weibel, Cecilia","contributorId":189756,"corporation":false,"usgs":false,"family":"Lopez Weibel","given":"Cecilia","email":"","affiliations":[],"preferred":false,"id":757710,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Guerrero, Massimo","contributorId":213431,"corporation":false,"usgs":false,"family":"Guerrero","given":"Massimo","email":"","affiliations":[{"id":38756,"text":"University of Bologna, Italy","active":true,"usgs":false}],"preferred":false,"id":757711,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Latosinski, Francisco","contributorId":213432,"corporation":false,"usgs":false,"family":"Latosinski","given":"Francisco","email":"","affiliations":[{"id":38757,"text":"Universidad Nacional del Litoral, Argentina","active":true,"usgs":false}],"preferred":false,"id":757712,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wood, Molly S. 0000-0002-5184-8306 mswood@usgs.gov","orcid":"https://orcid.org/0000-0002-5184-8306","contributorId":788,"corporation":false,"usgs":true,"family":"Wood","given":"Molly","email":"mswood@usgs.gov","middleInitial":"S.","affiliations":[{"id":37786,"text":"WMA - 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2019 - Evaluation of Chinook salmon (Oncorhynchus tshawytscha) fry survival at Lookout Point Reservoir, western Oregon, 2017","interactions":[],"lastModifiedDate":"2019-02-21T16:47:19","indexId":"ofr20191011","displayToPublicDate":"2019-02-21T08:44:09","publicationYear":"2019","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":"2019-1011","displayTitle":"Evaluation of Chinook Salmon (Oncorhynchus tshawytscha) Fry Survival in Lookout Point Reservoir, Western Oregon, 2017","title":"Evaluation of Chinook salmon (Oncorhynchus tshawytscha) fry survival at Lookout Point Reservoir, western Oregon, 2017","docAbstract":"A field study was conducted to estimate survival of fry-sized juvenile Chinook salmon (Oncorhynchus tshawytscha) in Lookout Point Reservoir, western Oregon, during 2017. The field study consisted of releasing three groups of genetically marked fish in the reservoir and monthly fish sampling. Fish were released during April 18–19 (43,950 fish), May 30–June 2 (44,145 fish), and on June 28, 2017 (3,920 fish). Reservoir sampling began in May and occurred monthly through October, consisting of 5-day events where juvenile Chinook salmon were collected using various gear types (electrofishing, shoreline traps, gill nets). Data were analyzed using two models: (1) a staggered release-recovery model (SRRM), and (2) a parentage-based tagging (PBT) N-mixture model. The SRRM provided survival estimates from two periods: (1) mid-April to late May (SSRRM1), and (2) late May to late June (SSRRM2). Multiple estimates of survival were possible for each period using different combinations of recovery data from the three groups of fish that were released. Survival estimates for SSRRM1 ranged from 0.470 to 0.520. Estimates for SSRRM2 ranged from 0.968 to 0.969; cumulative survival from mid-April to late June (SSRRM2) was estimated at 0.870. We suspect that issues with the third release group led to biased survival results using the SRRM. The PBT N-mixture model provided survival estimates from six periods: (1) mid-April to mid-May (SNMIX1), (2) mid-May to mid-June (SNMIX2), (3) mid-June to mid-July (SNMIX3), (4) mid-July to mid-August (SNMIX4), (5) mid-August to mid-September (SNMIX5), and (6) mid-September to mid-October (SNMIX6). Survival estimates from the PBT N-mixture model were lowest for SNMIX1 (0.461) and increased monthly to a high of 0.970 for SNMIX6. Cumulative survival from mid-April to mid-July was 0.233 and overall survival from mid-April to mid-October was 0.188. This suggests that most mortality occurred early in the study when juvenile Chinook salmon were small. This could be because these fish were most vulnerable to predation in the reservoir at that time. We determined that mortality of juvenile Chinook salmon was high in the reservoir during this study and similar estimates of parr-to-smolt survival have been observed in other systems. Additional analyses are required, including results from the second year of study (2018), and potentially similar evaluations will need to be made at other locations to determine if reservoir mortality is a limiting survival factor for Chinook salmon in the Middle Fork Willamette River.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191011","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers and Oregon State University","usgsCitation":"Kock, T.J., Perry, R.W., Hansen, G.S., Haner, P.V., Pope, A.C., Plumb, J.M., Cogliati, K.M., and Hansen, A.C., 2019, Evaluation of Chinook salmon (Oncorhynchus tshawytscha) fry survival at Lookout Point Reservoir, western Oregon, 2017: U.S. Geological Survey Open-File Report 2019-1011, 42 p., https://doi.org/10.3133/ofr20191011.","productDescription":"vi, 42 p.","numberOfPages":"52","onlineOnly":"Y","ipdsId":"IP-102234","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":361397,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1011/coverthb.jpg"},{"id":361398,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1011/ofr20191011.pdf","text":"Report","size":"12.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1011"}],"country":"United States","state":"Oregon","otherGeospatial":"Lookout Point Reservoir","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.89718627929688,\n 43.91768033000405\n ],\n [\n -122.81410217285155,\n 43.98589821991874\n ],\n [\n -122.64244079589842,\n 43.929055415997134\n ],\n [\n -122.5140380859375,\n 43.82808744469062\n ],\n [\n -122.63626098632812,\n 43.77059798257491\n ],\n [\n -122.75711059570312,\n 43.854830911225235\n ],\n [\n -122.89718627929688,\n 43.91768033000405\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
The Souris River Basin is a 24,000 square-mile basin in the Provinces of Saskatchewan and Manitoba in Canada, and the State of North Dakota in the United States. Above-average snowpack during the winter of 2010–11, along with record-setting rains in May and June of 2011, led to record flooding that caused extensive damage to Minot, North Dakota, and numerous smaller communities in Saskatchewan, Manitoba, and North Dakota. As a result, the International Souris River Board created the Souris River Flood Task Force to evaluate potential reservoir operation changes and flood control measures to manage future floods and droughts. Part of this evaluation involved identifying a need for a stochastic streamflow model to estimate the likelihood of future flooding or drought.
A stochastic natural (unregulated) streamflow simulation model described in a previous report was built upon in this report to include the effects of regulation of four reservoirs (Rafferty, Alameda, and Boundary Reservoirs and Lake Darling) and their operation guidelines. First, a regulated reservoir storage/streamflow routing model was developed and calibrated from when all four reservoirs were in operation until the end of the reconstructed natural streamflow dataset provided by the U.S. Army Corps of Engineers (1992–2011). The regulated reservoir storage/streamflow routing model then was combined with the stochastic natural (unregulated) streamflow model to provide a stochastic regulated streamflow simulation model for the Souris River Basin upstream from Minot, North Dakota.
The stochastic regulated streamflow simulation model was used to estimate regulated flood frequency curves, which are useful for feasibility and design of critical structures such as levees or bridges. Three potential future climatic conditions were considered in this analysis: condition A (wet equilibrium), representing wet (similar to 1970–2017) climatic conditions; condition B (transition), representing transition from wet to dry (similar to 1912–69) climatic conditions; and condition C (dry equilibrium), representing dry climatic conditions. Comparison of the estimated flood frequency curves for regulated flow among the three climatic conditions indicated large differences in flood magnitudes for the more extreme (1-percent or less) annual exceedance probabilities. The estimated 0.2-percent annual exceedance probability flood magnitude for the Souris River upstream from Minot, N. Dak., was 29,300 cubic feet per second for condition A (wet equilibrium), compared to 14,800 cubic feet per second for condition C (dry equilibrium). For comparison, the recorded peak streamflow for 2011 for the Souris River upstream from Minot, N. Dak., was 26,900 cubic feet per second. Although it is not possible to predict how long the current (1970–2017) wet climatic conditions may persist, flood risk for at least the next 25 years, or until about 2040, may be represented best by climatic condition A.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185155","collaboration":"Prepared in cooperation with the North Dakota State Water Commission","usgsCitation":"Kolars, K.A., Vecchia, A.V., and Galloway, J.M., 2019, Stochastic model for simulating Souris River Basin regulated streamflow upstream from Minot, North Dakota: U.S. Geological Survey Scientific Investigations Report 2018–5155, 24 p., https://doi.org/10.3133/sir20185155.","productDescription":"viii, 24 p.","numberOfPages":"36","onlineOnly":"Y","ipdsId":"IP-090130","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":361373,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5155/coverthb.jpg"},{"id":361374,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5155/sir20185155.pdf","text":"Report","size":"1.75 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5155"}],"country":"United States","state":"North Dakota","city":"Minot","otherGeospatial":"Souris River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.04052734375,\n 48.99824008113872\n ],\n [\n -104.74365234375,\n 49.42884000063522\n ],\n [\n -104.7930908203125,\n 50.004208515595614\n ],\n [\n -103.480224609375,\n 50.52041218671901\n ],\n [\n -102.0245361328125,\n 50.604159488561\n ],\n [\n -101.195068359375,\n 50.25071752130677\n ],\n [\n -100.65673828125,\n 49.745781306155735\n ],\n [\n -99.60891723632812,\n 49.648069803718805\n ],\n [\n -99.18594360351562,\n 49.577773933420914\n ],\n [\n -99.2340087890625,\n 49.39131220507362\n ],\n [\n -99.76547241210936,\n 49.413653634531116\n ],\n [\n -99.4482421875,\n 48.100094697973795\n ],\n [\n -101.502685546875,\n 47.99727386804474\n ],\n [\n -103.568115234375,\n 48.52388120259336\n ],\n [\n -104.04052734375,\n 48.99824008113872\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503
The recovery of endangered Lost River suckers (Deltistes luxatus) in Upper Klamath Lake, south-central Oregon, has been impeded because juveniles are not recruiting into adult spawning populations. Adult sucker populations spawn each spring but mortality of age-0 suckers during their first summer is excessively high, and recruitment of juveniles into adult populations does not occur in most years. The last significant year class to join spawning aggregations was hatched in 1991. Capture rates for age-0 Lost River suckers decrease so substantially each summer that it is thought that mortality is nearly 100 percent within the first year of life each year. Causes of mortality are not understood but poor water quality, parasites, disease, predation, and non-native species are suspected to contribute to mortality. Upper Klamath Lake is hypereutrophic and summer water-quality conditions have large diurnal and seasonal fluctuations. Photosynthesis of Aphanizomenon flos-aquae, the most abundant cyanobacterium in Upper Klamath Lake, is responsible for large fluctuations in dissolved-oxygen (DO) concentrations and pH.
We introduced hatchery-raised, passive integrated transponder-tagged juvenile Lost River suckers into large mesocosms located at Fish Banks, Mid North, and Rattlesnake Point in Upper Klamath Lake, Oregon, to assess sucker mortality relative to water-quality conditions. We identified the date of death for each sucker by assessing movement patterns among vertically stratified antennas. We modeled daily mortality using known fate models relative to water-quality conditions measured by sondes. Histopathology was used to understand causes of eminent mortality for moribund suckers.
Fish mortality, growth, health, and movement patterns varied among locations, but it was unclear whether this variation was due to water-quality or other factors. Seasonal mortality was 58.8 percent at Fish Banks, 27.4 percent at Mid North, and 11.5 percent at Rattlesnake Point. Growth over the 109-day study period was lowest at Fish Banks (34.5 ±10.0 millimeters [mm] standard length (SL); 18.6 ±7.7 grams [g]), intermediate at Mid North (57.5 ±13.6 mm SL; 40.1 ±15.4 g), and greatest at Rattlesnake Point (78.4 ±13.0 mm SL; 72.5 ±18.7 g). Our ability to assess causes of juvenile sucker mortality in mesocosms using our modelling approach was limited by low daily mortality. Zero to 3 mortalities occurred per day, except on July 30 at Fish Banks when 7 mortalities occurred. Relative to any other measured and tested water-quality condition, mortality was more likely to occur on days with large fluctuations in oxygen percent saturation. When we assessed the fit of the most parsimonious model, performance was poor, which suggested that other factors were contributing to mortality. Our ability to assess the relationship between seasonal patterns in water quality and fish mortality were limited by the absence of substantial differences in water quality among sites, inconsistency in the depth at which measurements were collected, and no clear pattern in conditions leading up to and during mortality events. Except for DO at Rattlesnake Point and diel temperature variations at Fish Banks, seasonally summarized water-quality factors were similar among sites. The locations of water-quality monitors within the water column likely explain the differences in DO at Rattlesnake Point and temperature variation at Fish Banks. Furthermore, DO concentrations and other water-quality factors occurring during and prior to mortality events were inconsistent.
Microscopic assessments indicated severe gill hyperplasia, fusion of the secondary lamellae, and severe Ichthyobodo sp. infestations on the gills of most moribund suckers. Liver glycogen was usually depleted in suckers with severe Ichthyobodo sp. infestations. Ichthyobodo sp. infestations probably were the immediate cause of death and probably originated from the Klamath Tribes Fish Research Facility, although this parasite also is present in Upper Klamath Lake and severe water-quality conditions may have contributed to morbidity. As suckers in the mesocosms died, they were replaced with suckers from the Fish Research Facility that likely were heavily parasitized with Ichthyobodo sp. Therefore, it is possible that the gradient in mortality rate among sites was owing to site-varying differences in inadvertent increases in introduced parasite loads.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191006","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Hereford, D.M., Conway, C.M., Burdick, S.M., Elliott, D.G., Perry, T.M., Dolan-Caret, A., and Harris, A.C., 2019, Assessing causes of mortality for endangered juvenile Lost River suckers (Deltistes luxatus) in mesocosms in Upper Klamath Lake, south-central Oregon, 2016: U.S. Geological Survey Open -File Report 2019-1006, 80 p., https://doi.org/10.3133/ofr20191006.","productDescription":"viii, 80 p.","numberOfPages":"92","onlineOnly":"Y","ipdsId":"IP-098400","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":361283,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1006/ofr20191006.pdf","text":"Report","size":"12.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1006"},{"id":361282,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1006/coverthb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Upper Klamath Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.10273742675781,\n 42.22750046697999\n ],\n [\n -121.79374694824219,\n 42.22750046697999\n ],\n [\n -121.79374694824219,\n 42.595554553719204\n ],\n [\n -122.10273742675781,\n 42.595554553719204\n ],\n [\n -122.10273742675781,\n 42.22750046697999\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
The morphodynamics of river-dominated deltas are largely controlled by the supply and retention of sediment within deltaic wetlands and the rate of relative sea-level rise. Yet, sediment budgets for deltas are often poorly constrained. In the Mississippi River Delta, a system rapidly losing land due to natural and anthropogenic causes, restoration efforts seek to build new land through the use of river diversions. At the Davis Pond Freshwater Diversion, a new crevasse splay has emerged since construction was completed in 2002. Here, we use beryllium-7 activity in sediment cores and USGS measurements of discharge and turbidity to calculate seasonal sediment input, deposition, and retention within the vegetated Davis Pond receiving basin. In winter/spring 2015, which included an experimental period of high discharge through the diversion, Davis Pond received 106,800 metric tons of sediment, 44% of which was retained within the basin. During this time, mean flow velocity was 0.21 m s−1 and mean turbidity was 56 formazin nephelometric units (FNU). In summer/fall 2015, the Davis Pond basin received 35,900 metric tons of sediment, 81% of which was retained. Mean flow velocity in summer/fall was 0.10 m s−1 and mean turbidity was 55 FNU. The increase in sediment retention from winter/spring 2015 to summer/fall 2015 may be due in part to the corresponding drop in water flow velocity, which allowed more sediment to settle out of suspension. Although high water discharge increases sediment input and deposition, increased turbulence associated with higher current velocity appears to increase sediment throughput and thereby decrease the sediment trapping efficiency. Sediment retention in Davis Pond is on the high end of the range seen in deltaic wetlands, perhaps due to the enclosed geometry of the receiving basin. Future diversion design and operation should target moderate water discharge and flow velocities in order to jointly maximize sediment deposition and retention and provide optimal conditions for delta growth.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.geomorph.2019.02.008","usgsCitation":"Keogh, M.E., Kolker, A.S., Snedden, G., and Renfro, A.A., 2019, Hydrodynamic controls on sediment retention in an emerging diversion-fed delta: Geomorphology, v. 332, p. 100-111, https://doi.org/10.1016/j.geomorph.2019.02.008.","productDescription":"12 p.","startPage":"100","endPage":"111","ipdsId":"IP-092068","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":467886,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.geomorph.2019.02.008","text":"Publisher Index Page"},{"id":437565,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9V7N49P","text":"USGS data release","linkHelpText":"Mineral content, bulk density, and beryllium-7 activity of wetland soils of the Davis Pond Freshwater Diversion Outfall Area, Louisiana, in 2015"},{"id":361386,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","otherGeospatial":"Mississippi River Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.36529541015625,\n 29.844217466091493\n ],\n [\n -90.2362060546875,\n 29.844217466091493\n ],\n [\n -90.2362060546875,\n 29.963857983730453\n ],\n [\n -90.36529541015625,\n 29.963857983730453\n ],\n [\n -90.36529541015625,\n 29.844217466091493\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"332","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Keogh, Molly E.","contributorId":213408,"corporation":false,"usgs":false,"family":"Keogh","given":"Molly","email":"","middleInitial":"E.","affiliations":[{"id":13500,"text":"Tulane University","active":true,"usgs":false}],"preferred":false,"id":757660,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kolker, Alexander S.","contributorId":213409,"corporation":false,"usgs":false,"family":"Kolker","given":"Alexander","email":"","middleInitial":"S.","affiliations":[{"id":38749,"text":"Tulane University; Louisiana Universities Marine Consortium","active":true,"usgs":false}],"preferred":false,"id":757661,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Snedden, Gregg A. 0000-0001-7821-3709","orcid":"https://orcid.org/0000-0001-7821-3709","contributorId":212275,"corporation":false,"usgs":true,"family":"Snedden","given":"Gregg","middleInitial":"A.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":757659,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Renfro, Alisha A.","contributorId":213410,"corporation":false,"usgs":false,"family":"Renfro","given":"Alisha","email":"","middleInitial":"A.","affiliations":[{"id":38750,"text":"National Wildlife Federation, Mississippi River Delta Campaign","active":true,"usgs":false}],"preferred":false,"id":757662,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70202282,"text":"70202282 - 2019 - Dynamic N-mixture models with temporal variability in detection probability","interactions":[],"lastModifiedDate":"2019-02-20T10:44:50","indexId":"70202282","displayToPublicDate":"2019-02-20T10:44:45","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"title":"Dynamic N-mixture models with temporal variability in detection probability","docAbstract":"In theory parameters of dynamic N-mixture models can be estimated with multiple years of data without the robust design under the assumption of constant detection probability. However, such an assumption can rarely be met in long-term studies, and the consequences of violating this assumption in the inferences of dynamic N-mixture models have not been assessed. In this study we used simulation studies to evaluate inferences of the original dynamic N-mixture model and two of its spatial extensions in the face of temporal variability in detection probability. We first evaluated the dynamic N-mixture models when detection probability that varied temporally was wrongly treated as a constant. We then evaluated if the robust design was necessary for dynamic N-mixture models to provide valid parameter estimates when detection probability was correctly assumed to vary temporally. Our results showed that, when detection probability that varied temporally was wrongly treated as a constant, biases were introduced in the parameter estimates of dynamic N-mixture models. When detection probability was correctly assumed to vary temporally, the models could provide valid parameter estimates with the robust design. The model could also provide valid parameter estimates when detection probability was a random effect, even without the robust design. Based on our results, we strongly recommended considering temporal variability in detection probability when using dynamic N-mixture models to analyze long-term data and adopting the robust design in long-term surveys. Our work here is not only useful for data analysis but also important for research design, and thus are relevant to a wide range of studies.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2018.12.007","usgsCitation":"Zhao, Q., and Royle, J.A., 2019, Dynamic N-mixture models with temporal variability in detection probability: Ecological Modelling, v. 393, p. 20-24, https://doi.org/10.1016/j.ecolmodel.2018.12.007.","productDescription":"5 p.","startPage":"20","endPage":"24","ipdsId":"IP-103124","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":361376,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"393","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Zhao, Qing","contributorId":213406,"corporation":false,"usgs":false,"family":"Zhao","given":"Qing","email":"","affiliations":[{"id":34045,"text":"Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, CO 80523","active":true,"usgs":false}],"preferred":false,"id":757623,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":139626,"corporation":false,"usgs":true,"family":"Royle","given":"J.","email":"aroyle@usgs.gov","middleInitial":"Andrew","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":757622,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70223367,"text":"70223367 - 2019 - Socioecological determinants of drought impacts and coping strategies for ranching operations in the Great Plains","interactions":[],"lastModifiedDate":"2021-08-25T13:24:49.325426","indexId":"70223367","displayToPublicDate":"2019-02-20T08:21:58","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3228,"text":"Rangeland Ecology and Management","onlineIssn":"1551-5028","printIssn":"1550-7424","active":true,"publicationSubtype":{"id":10}},"title":"Socioecological determinants of drought impacts and coping strategies for ranching operations in the Great Plains","docAbstract":"In Great Plains rangelands, drought is a recurring disturbance. Ranchers in this region expect to encounter drought but may not be adequately prepared for it. Efforts to encourage drought preparedness would benefit from a better understanding of the conditions under which managers make decisions to minimize the impacts of drought. We tested the direct and moderating roles of the drought hazard and the social-ecological context on drought impacts and response. This study was conducted with ranchers in western and central South Dakota and Nebraska following the drought that began in 2012. We surveyed ranchers regarding the effects of the drought and their responses and used multimodel analysis to explore the relationships among measures of drought preparedness, drought response, and drought impacts. Drought severity was the primary predictor of all impacts, but specific types of impacts were varied depending on the operation’s enterprise mix, resources, and management. The socioecological characteristics of the ranch system predicted drought response actions taken, by either providing the necessary resources and capacity to take action or creating sensitivity in the system that required action to be taken. We conclude with recommendations for learning from current drought experiences in order to better adapt to future drought events.
Southern New England provides key wintering habitat for White-winged Scoters (Melanitta fusca). This area has also pioneered the development of offshore wind energy in North America and the U.S. Bureau of Ocean Energy Management (BOEM) has established nine Wind Energy Area (WEA) lease blocks along the Atlantic Outer Continental Shelf in areas that may provide important staging and wintering habitat for scoters and other species of sea ducks. Concern over the potential impact of offshore wind energy on sea duck populations has led to efforts to develop models to understand their distribution, habitat use and site fidelity. We used satellite telemetry to document winter phenology and site fidelity, as well as fine-scale resource selection and habitat use of 40 White-winged Scoters along the southern New England continental shelf. Scoters spent over half of the annual cycle on the wintering grounds and demonstrated a high degree of inter-annual site fidelity to composite core-use areas. Sizes of individual 50% core-use home ranges were variable (x̅ = 868 km2; range = 32 to 4,220 km2) and individual 95% utilization distributions ranged widely (x̅ = 4,388 km2; range = 272 to 18,235 km2). More than half of all tagged birds occupied two or more discrete core-use areas that were up to 400 km apart. Throughout the study area, scoters selected for areas with lower salinity, lower sea surface temperature, higher chlorophyll-a concentrations, and higher hard-bottom substrate probability. Resource selection function models classified 18,649 km2 (23%) of the study area as high probability of use, which included or immediately bordered ~420 km2 of proposed WEA lease blocks. Future offshore wind energy developments in the region should avoid key habitats highlighted by this study and carefully consider the environmental characteristics selected by sea ducks when planning and siting future WEAs.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/condor/duy014","usgsCitation":"Meattey, D.E., McWilliams, S.R., Paton, P.W., Lepage, C., Gilliland, S.G., Savoy, L., Olsen, G.H., and Osenkowski, J.E., 2019, Resource selection and wintering phenology of White-winged Scoters in southern New England: Implications for offshore wind energy development: Condor, v. 121, no. 1, duy014, https://doi.org/10.1093/condor/duy014.","productDescription":"duy014","ipdsId":"IP-090294","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":488696,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://digitalcommons.uri.edu/nrs_facpubs/529","text":"External Repository"},{"id":375459,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Southern New England","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.3447265625,\n 40.84706035607122\n ],\n [\n -69.9169921875,\n 40.84706035607122\n ],\n [\n -69.9169921875,\n 43.866218006556394\n ],\n [\n -73.3447265625,\n 43.866218006556394\n ],\n [\n -73.3447265625,\n 40.84706035607122\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"121","issue":"1","noUsgsAuthors":false,"publicationDate":"2019-02-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Meattey, Dustin E.","contributorId":225141,"corporation":false,"usgs":false,"family":"Meattey","given":"Dustin","email":"","middleInitial":"E.","affiliations":[{"id":41045,"text":"Department of Natural Resources Sciences, University of Rhode Island, Kingston, RI","active":true,"usgs":false}],"preferred":false,"id":790515,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McWilliams, Scott R.","contributorId":172328,"corporation":false,"usgs":false,"family":"McWilliams","given":"Scott","email":"","middleInitial":"R.","affiliations":[{"id":6922,"text":"University of Rhode Island","active":true,"usgs":false}],"preferred":false,"id":790516,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Paton, Peter W.C.","contributorId":225142,"corporation":false,"usgs":false,"family":"Paton","given":"Peter","email":"","middleInitial":"W.C.","affiliations":[{"id":41045,"text":"Department of Natural Resources Sciences, University of Rhode Island, Kingston, RI","active":true,"usgs":false}],"preferred":false,"id":790517,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lepage, Christine","contributorId":194564,"corporation":false,"usgs":false,"family":"Lepage","given":"Christine","email":"","affiliations":[],"preferred":false,"id":790518,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gilliland, Scott G.","contributorId":225143,"corporation":false,"usgs":false,"family":"Gilliland","given":"Scott","email":"","middleInitial":"G.","affiliations":[{"id":41046,"text":"Canadian Wildlife Service, Environment and Climate Change Canada, Sackville, NB","active":true,"usgs":false}],"preferred":false,"id":790519,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Savoy, Lucas","contributorId":171896,"corporation":false,"usgs":false,"family":"Savoy","given":"Lucas","affiliations":[{"id":6928,"text":"BioDiversity Research Institute, Gorham, ME 04038","active":true,"usgs":false}],"preferred":false,"id":790612,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Olsen, Glenn H. 0000-0002-7188-6203 golsen@usgs.gov","orcid":"https://orcid.org/0000-0002-7188-6203","contributorId":40918,"corporation":false,"usgs":true,"family":"Olsen","given":"Glenn","email":"golsen@usgs.gov","middleInitial":"H.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":790520,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Osenkowski, Jason E.","contributorId":225144,"corporation":false,"usgs":false,"family":"Osenkowski","given":"Jason","email":"","middleInitial":"E.","affiliations":[{"id":41047,"text":"Rhode Island Department of Environmental Management, West Kingston, RI","active":true,"usgs":false}],"preferred":false,"id":790521,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70202058,"text":"tm7C21 - 2019 - User’s guide for Assessment Tract Aggregation GUI (ATA GUI)—A graphical user interface for the AggtEx.fn R script","interactions":[],"lastModifiedDate":"2019-03-12T11:01:57","indexId":"tm7C21","displayToPublicDate":"2019-02-19T15:15:00","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"7-C21","displayTitle":"User’s Guide for Assessment Tract Aggregation GUI (ATA GUI)—A Graphical User Interface for the AggtEx.fn R Script","title":"User’s guide for Assessment Tract Aggregation GUI (ATA GUI)—A graphical user interface for the AggtEx.fn R script","docAbstract":"The U.S. Geological Survey three-part method for mineral resource assessments estimates numbers of undiscovered mineral deposits as probability distributions in geologically defined regions termed “permissive tracts.” This report describes a graphical user interface (GUI) script developed in open-source statistical software (R) that aggregates estimated undiscovered deposits of a given type from two or more permissive tracts using the AggtEx.fn R script. The AggtEx.fn R script aggregates undiscovered deposit estimates assuming independence, total dependence, or some degree of correlation among aggregated areas, given a user-specified correlation matrix. The script outputs three sets of aggregated estimates based on those three assumptions.
The GUI script described in this report, Assessment Tract Aggregation GUI (ATA GUI), provides an easy-to-use tool that supports implementation of the AggtEx.fn R script, installation of the R packages needed to run the application, and creation of a combined input file from individual files generated by the MapMark4GUI software. Users can also use EMINERS output information by creating a file of output values following the MapMark4GUI output file format. The probabilistic estimates of aggregated undiscovered deposits produced by ATA GUI can be used as input for MapMark4GUI to estimate contained resources for the aggregated tracts. MapMark4GUI uses Monte Carlo simulation to combine undiscovered deposit estimates with tonnage and grade models to simulate undiscovered mineral resources for a region of interest. This simulation includes the amounts of commodities and rock that could be present within a permissive tract. This report includes instructions on installing and running the ATA GUI script and describes the input and output files used and created during the aggregation process.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section C: Computer programs in Book 7: Automated data processing and computations","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm7C21","collaboration":" ","usgsCitation":"Shapiro, J.L., and Robinson, G.R., Jr., 2019, User’s guide for Assessment Tract Aggregation GUI (ATA GUI)—A graphical user interface for the AggtEx.fn R script: U.S. Geological Survey Techniques and Methods, book 7, chap. C21, 9 p., https://doi.org/10.3133/tm7c21.","productDescription":"Report: iv, 9 p.; Assessment Tract Aggregation GUI Package","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-098557","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":361327,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/07/c21/coverthb.jpg"},{"id":361328,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/07/c21/tmc721.pdf","text":"Report","size":"1.04 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 7-C21"},{"id":361329,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/tm/07/c21/tm7c21_ATAGUI_Package.zip","text":"Assessment Tract Aggregation GUI Package","size":"751 KB","linkFileType":{"id":6,"text":"zip"}}],"contact":"Eastern Mineral and Environmental Resources Science Center
U.S. Geological Survey
12201 Sunrise Valley Drive
954 Mail Stop
Reston, VA 20192
In order to produce useful hydrologic and aquatic habitat data from the Landsat system, the U.S. Geological Survey has developed the “Dynamic Surface Water Extent” (DSWE) Landsat Science Product. DSWE will provide long-term, high-temporal resolution data on variations in inundation extent. The model used to generate DSWE is composed of five decision-rule based tests that do not require scene-based training. To allow its general application, required inputs are limited to the Landsat at-surface reflectance product and a digital elevation model. Unlike other Landsat-based water products, DSWE includes pixels that are only partially covered by water to increase inundation dynamics information content. Previously published DSWE model development included one wetland-focused test developed through visual inspection of field-collected Everglades spectra. A comparison of that test’s output against Everglades Depth Estimation Network (EDEN) in situ data confirmed the expectation that omission errors were a prime source of inaccuracy in vegetated environments. Further evaluation exposed a tendency toward commission error in coniferous forests. Improvements to the subpixel level “partial surface water” (PSW) component of DSWE was the focus of this research. Spectral mixture models were created from a variety of laboratory and image-derived endmembers. Based on the mixture modeling, a more “aggressive” PSW rule improved accuracy in herbaceous wetlands and reduced errors of commission elsewhere, while a second “conservative” test provides an alternative when commission errors must be minimized. Replication of the EDEN-based experiments using the revised PSW tests yielded a statistically significant increase in mean overall agreement (4%, p = 0.01, n = 50) and a statistically significant decrease (11%, p = 0.009, n = 50) in mean errors of omission. Because the developed spectral mixture models included image-derived vegetation endmembers and laboratory spectra for soil groups found across the US, simulations suggest where the revised DSWE PSW tests perform as they do in the Everglades and where they may prove problematic. Visual comparison of DSWE outputs with an unusual variety of coincidently collected images for locations spread throughout the US support conclusions drawn from Everglades quantitative analyses and highlight DSWE PSW component strengths and weaknesses.
","language":"English","publisher":"MDPI","doi":"10.3390/rs11040374","usgsCitation":"Jones, J., 2019, Improved automated detection of subpixel-scale inundation – Revised Dynamic Surface Water Extent (DSWE) partial surface water tests: Remote Sensing, v. 11, no. 4, p. 1-26, https://doi.org/10.3390/rs11040374.","productDescription":"Article 374; 26 p.","startPage":"1","endPage":"26","ipdsId":"IP-102379","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":467892,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs11040374","text":"Publisher Index Page"},{"id":361339,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"4","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2019-02-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Jones, John W. 0000-0001-6117-3691 jwjones@usgs.gov","orcid":"https://orcid.org/0000-0001-6117-3691","contributorId":2220,"corporation":false,"usgs":true,"family":"Jones","given":"John","email":"jwjones@usgs.gov","middleInitial":"W.","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"preferred":true,"id":757437,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70202260,"text":"70202260 - 2019 - Estimating uncertainty of North American landbird population sizes","interactions":[],"lastModifiedDate":"2019-02-19T11:38:08","indexId":"70202260","displayToPublicDate":"2019-02-19T11:38:03","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":947,"text":"Avian Conservation and Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Estimating uncertainty of North American landbird population sizes","docAbstract":"An important metric for many aspects of species conservation planning and risk assessment is an estimate of total population size. For landbirds breeding in North America, Partners in Flight (PIF) generates global, continental, and regional population size estimates. These estimates are an important component of the PIF species assessment process, but have also been used by others for a range of applications. The PIF population size estimates are primarily calculated using a formula designed to extrapolate bird counts recorded by the North American Breeding Bird Survey (BBS) to regional population estimates. The extrapolation formula includes multiple assumptions and sources of uncertainty, but there were previously no attempts to quantify this uncertainty in the published population size estimates aside from a categorical data quality score. Using a Monte Carlo approach, we propagated the main sources of uncertainty arising from individual components of the model through to the final estimation of landbird population sizes. This approach results in distributions of population size estimates rather than point estimates. We found the width of uncertainty of population size estimates to be generally narrower than the order-of-magnitude distances between the population size score categories PIF uses in the species assessment process, suggesting confidence in the categorical ranking used by PIF. Our approach provides a means to identify species whose uncertainty bounds span more than one categorical rank, which was not previously possible with the data quality scores. Although there is still room for additional improvements to the estimation of avian population sizes and uncertainty, particularly with respect to replacing categorical model components with empirical estimates, our estimates of population size distributions have broader utility to a range of conservation planning and risk assessment activities relying on avian population size estimates.
","language":"English","publisher":"American Ornithological Society","doi":"10.5751/ACE-01331-140104","usgsCitation":"Stanton, J.C., Blancher, P.J., Rosenberg, K.V., Panjabi, A.O., and Thogmartin, W.E., 2019, Estimating uncertainty of North American landbird population sizes: Avian Conservation and Ecology, v. 14, no. 1, Article 4; 16 p., https://doi.org/10.5751/ACE-01331-140104.","productDescription":"Article 4; 16 p.","ipdsId":"IP-090781","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":467893,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5751/ace-01331-140104","text":"Publisher Index Page"},{"id":437569,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90SWVFU","text":"USGS data release","linkHelpText":"Population Size uncertainty estimates"},{"id":361335,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","issue":"1","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Stanton, Jessica C. 0000-0002-6225-3703 jcstanton@usgs.gov","orcid":"https://orcid.org/0000-0002-6225-3703","contributorId":5634,"corporation":false,"usgs":true,"family":"Stanton","given":"Jessica","email":"jcstanton@usgs.gov","middleInitial":"C.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":757536,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Blancher, Peter J.","contributorId":175182,"corporation":false,"usgs":false,"family":"Blancher","given":"Peter","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":757537,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rosenberg, Kenneth V.","contributorId":171463,"corporation":false,"usgs":false,"family":"Rosenberg","given":"Kenneth","email":"","middleInitial":"V.","affiliations":[{"id":27615,"text":"Cornell Lab of Ornithology, Conservation Science Program","active":true,"usgs":false}],"preferred":false,"id":757538,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Panjabi, Arvind O.","contributorId":169967,"corporation":false,"usgs":false,"family":"Panjabi","given":"Arvind","email":"","middleInitial":"O.","affiliations":[{"id":25644,"text":"Bird Conservancy of the Rockies","active":true,"usgs":false}],"preferred":false,"id":757539,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thogmartin, Wayne E. 0000-0002-2384-4279 wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":757540,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70200151,"text":"sir20185125 - 2019 - Potential for increased inundation in flood-prone regions of southeast Florida in response to climate and sea-level changes in Broward County, Florida, 2060–69","interactions":[],"lastModifiedDate":"2019-02-19T14:54:42","indexId":"sir20185125","displayToPublicDate":"2019-02-19T11:28:48","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-5125","displayTitle":"Potential for Increased Inundation in Flood-Prone Regions of Southeast Florida in Response to Climate and Sea-Level Changes in Broward County, Florida, 2060–69","title":"Potential for increased inundation in flood-prone regions of southeast Florida in response to climate and sea-level changes in Broward County, Florida, 2060–69","docAbstract":"The U.S. Geological Survey, in cooperation with Broward County Environmental Planning and Resilience Division, has developed county-scale and local-scale groundwater/surface-water models to study the potential for increased inundation and flooding in eastern Broward County that are due to changes in future climate and sea-level rise. These models were constructed by using MODFLOW 2005, with the surface-water system represented by using the Surface-Water Routing process and a new Urban Runoff process. The local-scale model allowed the use of finer grid resolution in a selected area of the county, whereas the county-scale model provided boundary conditions for the local-scale model and insight into the hydrologic behavior of the larger system. The aquifer layering, properties, and boundaries relied heavily on a previous three-dimensional variable-density solute-transport model of the same area developed by the U.S. Geological Survey. The surface-water system within these new models actively simulates a part of the extensive canal network by using level-pool routing and active structure operations within the Surface-Water Routing process. These models were used to simulate a historical base-case period (1990–99) by using measured data and regional climate model rainfall and potential evapotranspiration output. The simulated flow and water-level results generally captured the behavior of the hydrologic system. A future period (2060–69) was simulated by using regional climate model rainfall and potential evapotranspiration output representing a wetter and drier future and low, intermediate, and high sea-level rise projections. The results were used to evaluate the potential effects on the surface-water drainage system, coastal-structure operation, and wet-season groundwater levels.
Future period simulations using the county-scale model indicate that (1) the effects of the changing climate and sea level are much more evident in eastern and coastal areas of Broward County compared to western areas, with increases in groundwater level nearly equivalent to sea-level rise; (2) coastal groundwater-level increases are distributed farther inland in the wetter future scenarios than in the drier future scenarios; (3) water levels at the westernmost groundwater station locations exhibited little change caused by sea-level rise and showed more dependence on changes in precipitation; (4) there was a reduced west-to-east groundwater gradient with increasing sea-level rise; and (5) increased downstream tidal stage at the S–13 structure resulted in increased reliance on the pump to control upstream inland canal stages. Future simulations using the local-scale model indicate similar behavior as seen in the county-scale model: (1) the coastal areas exhibited the largest impacts in groundwater levels in the future scenarios; (2) the westernmost, interior areas exhibited little change during the future scenarios; and (3) there was an increased reliance on the pump at the S–13 coastal structure but to a lesser extent than indicated in the county-scale model because of the reduced temporal scale of the local-scale model.
Possible adaptation and mitigation strategies were simulated to evaluate the county-scale and local-scale models’ ability to simulate hydrologic changes. Alterations to S–13 pump operations within the county-scale model were tested, and results indicate a reduced effect of sea-level rise inland of the control structure, but the affected area is spatially limited. The concept of using pumps to reduce the local groundwater levels in two neighborhood-sized areas was tested by using the local-scale model. The MODFLOW 2005 Drain package was used to remove groundwater by using drainage elevations set to zero, 1 foot, and 2 feet above average wet-season groundwater levels. Area 1 was well connected to coastal boundaries, and a high rate of groundwater removal was required, whereas the rate of groundwater removal required was greatly reduced in Area 2, which is less connected to tidal boundaries. Water for these scenarios was assumed to be pumped to tide with no downstream effects.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185125","collaboration":"Prepared in cooperation with the Broward County Environmental Planning and Resilience Division","usgsCitation":"Decker, J.D., Hughes, J.D., and Swain, E.D., 2019, Potential for increased inundation in flood-prone regions of southeast Florida in response to climate and sea-level changes in Broward County, Florida, 2060–69: U.S. Geological Survey Scientific Investigations Report 2018–5125, 106 p., https://doi.org/10.3133/sir20185125.","productDescription":"Report: viii, 106 p.; Data Release","numberOfPages":"118","onlineOnly":"Y","ipdsId":"IP-066244","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":361163,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5125/sir20185125.pdf","text":"Report","size":"10.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5125"},{"id":361162,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5125/coverthb.jpg"},{"id":361164,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9E6INWZ","text":"USGS data release","description":"USGS Data Release","linkHelpText":"MODFLOW 2005 data sets for the simulation of potential increased inundation in flood-prone regions of Southeast Florida in response to climate and sea-level changes in Broward County, Florida, 2060–69"}],"country":"United States","state":"Florida","county":"Broward County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.44326782226562,\n 25.95557515483912\n ],\n [\n -80.07522583007812,\n 25.95557515483912\n ],\n [\n -80.07522583007812,\n 26.331576128197028\n ],\n [\n -80.44326782226562,\n 26.331576128197028\n ],\n [\n -80.44326782226562,\n 25.95557515483912\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
Aquatic features critical to watershed hydrology range widely in size from narrow, shallow streams to large, deep lakes. In this study we evaluated wetland, lake, and river systems across the Prairie Pothole Region to explore where pan-sharpened high-resolution (PSHR) imagery, relative to Landsat imagery, could provide additional data on surface water distribution and movement, missed by Landsat. We used the monthly Global Surface Water (GSW) Landsat product as well as surface water derived from Landsat imagery using a matched filtering algorithm (MF Landsat) to help consider how including partially inundated Landsat pixels as water influenced our findings. The PSHR outputs (and MF Landsat) were able to identify ~60–90% more surface water interactions between waterbodies, relative to the GSW Landsat product. However, regardless of Landsat source, by documenting many smaller (<0.2 ha), inundated wetlands, the PSHR outputs modified our interpretation of wetland size distribution across the Prairie Pothole Region.
The General Lake Model (GLM) is a one-dimensional open-source code designed to simulate the hydrodynamics of lakes, reservoirs, and wetlands. GLM was developed to support the science needs of the Global Lake Ecological Observatory Network (GLEON), a network of researchers using sensors to understand lake functioning and address questions about how lakes around the world respond to climate and land use change. The scale and diversity of lake types, locations, and sizes, and the expanding observational datasets created the need for a robust community model of lake dynamics with sufficient flexibility to accommodate a range of scientific and management questions relevant to the GLEON community. This paper summarizes the scientific basis and numerical implementation of the model algorithms, including details of sub-models that simulate surface heat exchange and ice cover dynamics, vertical mixing, and inflow–outflow dynamics. We demonstrate the suitability of the model for different lake types that vary substantially in their morphology, hydrology, and climatic conditions. GLM supports a dynamic coupling with biogeochemical and ecological modelling libraries for integrated simulations of water quality and ecosystem health, and options for integration with other environmental models are outlined. Finally, we discuss utilities for the analysis of model outputs and uncertainty assessments, model operation within a distributed cloud-computing environment, and as a tool to support the learning of network participants.
","language":"English","publisher":"European Geosciences Union","doi":"10.5194/gmd-12-473-2019","usgsCitation":"Hipsey, M.R., Bruce, L.C., Boon, C., Busch, B., Carey, C.C., Hamilton, D., Hanson, P.C., Read, J.S., de Sousa, E., Weber, M., and Winslow, L., 2019, A General Lake Model (GLM 3.0) for linking with high-frequency sensor data from the Global Lake Ecological Observatory Network (GLEON): Geoscientific Model Development, v. 12, p. 473-523, https://doi.org/10.5194/gmd-12-473-2019.","productDescription":"51 p.","startPage":"473","endPage":"523","ipdsId":"IP-091920","costCenters":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":467899,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/gmd-12-473-2019","text":"Publisher Index 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C.","contributorId":130969,"corporation":false,"usgs":false,"family":"Carey","given":"Cayelan","email":"","middleInitial":"C.","affiliations":[{"id":7185,"text":"Department of Biological Sciences, Virginia Tech, Blacksburg, VA, USA","active":true,"usgs":false}],"preferred":false,"id":757427,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hamilton, David P.","contributorId":166840,"corporation":false,"usgs":false,"family":"Hamilton","given":"David P.","affiliations":[{"id":24543,"text":"Environmental Research Institute, University of Waikato, Private Bag 3015, Hamilton 3240, New Zealand.","active":true,"usgs":false}],"preferred":false,"id":757428,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hanson, Paul C.","contributorId":35634,"corporation":false,"usgs":false,"family":"Hanson","given":"Paul","email":"","middleInitial":"C.","affiliations":[{"id":12951,"text":"Center for Limnology, University of Wisconsin Madison","active":true,"usgs":false}],"preferred":false,"id":757429,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Read, Jordan S. 0000-0002-3888-6631 jread@usgs.gov","orcid":"https://orcid.org/0000-0002-3888-6631","contributorId":4453,"corporation":false,"usgs":true,"family":"Read","given":"Jordan","email":"jread@usgs.gov","middleInitial":"S.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":160,"text":"Center for Integrated Data Analytics","active":false,"usgs":true},{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":757422,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"de Sousa, Eduardo","contributorId":213317,"corporation":false,"usgs":false,"family":"de Sousa","given":"Eduardo","email":"","affiliations":[{"id":38735,"text":"UWA School of Agriculture & Environment, The University of Western Australia","active":true,"usgs":false}],"preferred":false,"id":757430,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Weber, Michael","contributorId":213318,"corporation":false,"usgs":false,"family":"Weber","given":"Michael","affiliations":[],"preferred":false,"id":757431,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Winslow, Luke A. 0000-0002-8602-5510","orcid":"https://orcid.org/0000-0002-8602-5510","contributorId":211187,"corporation":false,"usgs":false,"family":"Winslow","given":"Luke A.","affiliations":[{"id":12656,"text":"Rensselaer Polytechnic Institute","active":true,"usgs":false}],"preferred":false,"id":757432,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70202737,"text":"70202737 - 2019 - Human-induced and natural carbon storage in floodplains of the Central Valley of California","interactions":[],"lastModifiedDate":"2019-03-25T09:16:40","indexId":"70202737","displayToPublicDate":"2019-02-15T10:56:13","publicationYear":"2019","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":"Human-induced and natural carbon storage in floodplains of the Central Valley of California","docAbstract":"Active floodplains can putatively store large amounts of organic carbon (SOC) in subsoils originating from catchment erosion processes with subsequent floodplain deposition. Our study focussed on the assessment of SOC pools associated with alluvial floodplain soils that are affected by human-induced changes in floodplain deposition and in situ SOC mineralisation due to land use change and drainage. We evaluated depth-dependent SOC contents based on 23 soil cores down to 3 m and 10 drillings down to 7 m in a floodplain area of the lower Cosumnes River. An estimate of 266 Mg C ha−1 or about 59% of the entire SOC stored within the 7 m profiles was found in the upper 2 m. Most profiles (n = 25) contained discrete buried A horizons at depths of approximately 0.8 m. These profiles had up to 130% higher SOC stocks. The mean δ13C of all deep soil profiles clearly indicated that arable land use has already altered the stable isotopic signature in the first meter of the profile. Radiocarbon dating showed that the 14C age in the buried horizon was younger than in overlaying soils indicating a substantial sedimentation phase for the overlaying soils. An additional analysis of total mercury contents in the soil profiles indicated that this sedimentation was associated with upstream hydraulic gold mining after the 1850s. In summary, deep alluvial soils in floodplains store large amounts of SOC not yet accounted for in global carbon models. Historic data give evidence that large amounts of sediment were transported into the floodplains of most rivers of the Central Valley and deposited over organically rich topsoil, which promoted the stabilization of SOC, and needs to be considered to improve our understanding of the human-induced interference with C cycling.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2018.09.205","usgsCitation":"Steger, K., Fiener, P., Marvin-DiPasquale, M.C., Viers, J.H., and Smart, D.R., 2019, Human-induced and natural carbon storage in floodplains of the Central Valley of California: Science of the Total Environment, v. 651, no. Part 1, p. 851-858, https://doi.org/10.1016/j.scitotenv.2018.09.205.","productDescription":"8 p.","startPage":"851","endPage":"858","ipdsId":"IP-094594","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":467900,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://nbn-resolving.org/urn:nbn:de:bvb:384-opus4-765705","text":"Publisher Index Page"},{"id":362276,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Central Valley","volume":"651","issue":"Part 1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Steger, Kristin 0000-0002-7737-0697","orcid":"https://orcid.org/0000-0002-7737-0697","contributorId":214369,"corporation":false,"usgs":false,"family":"Steger","given":"Kristin","email":"","affiliations":[{"id":39022,"text":"University of California, Davis CA","active":true,"usgs":false}],"preferred":false,"id":759732,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fiener, Peter","contributorId":214370,"corporation":false,"usgs":false,"family":"Fiener","given":"Peter","email":"","affiliations":[{"id":39023,"text":"Augsburg University, Augsburg, Germany","active":true,"usgs":false}],"preferred":false,"id":759733,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Marvin-DiPasquale, Mark C. 0000-0002-8186-9167 mmarvin@usgs.gov","orcid":"https://orcid.org/0000-0002-8186-9167","contributorId":1485,"corporation":false,"usgs":true,"family":"Marvin-DiPasquale","given":"Mark","email":"mmarvin@usgs.gov","middleInitial":"C.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":759731,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Viers, Joshua H.","contributorId":214371,"corporation":false,"usgs":false,"family":"Viers","given":"Joshua","email":"","middleInitial":"H.","affiliations":[{"id":39024,"text":"Univ. of California, Merced, CA","active":true,"usgs":false}],"preferred":false,"id":759734,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Smart, David R.","contributorId":214372,"corporation":false,"usgs":false,"family":"Smart","given":"David","email":"","middleInitial":"R.","affiliations":[{"id":39025,"text":"Univ. of California, Davis CA","active":true,"usgs":false}],"preferred":false,"id":759735,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70201100,"text":"ofr20181183 - 2019 - Design and methods of the U.S. Geological Survey Northeast Stream Quality Assessment (NESQA), 2016","interactions":[],"lastModifiedDate":"2019-02-15T14:02:05","indexId":"ofr20181183","displayToPublicDate":"2019-02-15T08:30:00","publicationYear":"2019","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":"2018-1183","displayTitle":"Design and Methods of the U.S. Geological Survey Northeast Stream Quality Assessment (NESQA), 2016","title":"Design and methods of the U.S. Geological Survey Northeast Stream Quality Assessment (NESQA), 2016","docAbstract":"During 2016, as part of the National Water-Quality Assessment Project (NAWQA), the U.S. Geological Survey conducted the Northeast Stream Quality Assessment (NESQA) to investigate stream quality in the northeastern United States. The goal of the NESQA was to assess the health of wadeable streams in the region by characterizing multiple water-quality factors that are stressors to aquatic life and by evaluating the relation between these stressors and the condition of biological communities. Urbanization, agriculture, and human modifications to streamflow are anthropogenic changes that greatly affect water quality in the region; consequently, the study design primarily selected sites and targeted stressors associated with these activities. The NESQA built on a prior NAWQA study conducted in the region in 2014, the Atlantic Highlands flow-ecology study, which investigated the effects of anthropogenically modified flows on aquatic biological communities in primarily forested watersheds. Land-cover data for the NESQA were used to identify and select sites within the region that had watersheds ranging in levels of urban and agricultural development. A total of 95 sites were selected: 67 on streams in watersheds representing a range of urban land use, 13 on streams in watersheds with some degree of agricultural land use, and 15 on streams in predominantly forested watersheds with little development. Depending on land-cover characteristics, sites were sampled weekly for metal and organic contaminants, nutrients, and sediment for either a 9-week period that began the week of June 6, 2016, or a 4-week period that begin the week of July 11, 2016. Beginning August 1, 2016, and for about 2 weeks, an ecological survey was conducted at every site to assess stream habitat, and algal, benthic invertebrate, and fish communities. Additional samples collected during the ecological surveys were streambed sediment for chemical analysis and toxicity testing, and fish tissue for mercury analysis. This report describes the various study components and methods of the NESQA and describes a precursor effort for the Atlantic Highlands flow-ecology study. Details are presented for measurements of water quality, sediment chemistry, streamflow, and ecological surveys of stream biota and habitat, as well as processes of sample analysis, quality assurance and quality control, and data management.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181183","collaboration":"National Water Quality Program","usgsCitation":"Coles, J.F., Riva-Murray, K., Van Metre, P.C., Button, D.T., Bell, A.H., Qi, S.L., Journey, C.A., and Sheibley, R.W., 2019, Design and methods of the U.S. Geological Survey Northeast Stream Quality Assessment (NESQA), 2016: U.S. Geological Survey Open-File Report 2018–1183, 46 p., https://doi.org/10.3133/ofr20181183.","productDescription":"Report: vii, 46 p.; Appendixes 1 and 2","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-095438","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":361093,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2018/1183/ofr20181183_appendix2.xlsx","text":"Appendix 2, tables 2.1 through 2.10: Excel ","size":"119 KB","linkFileType":{"id":3,"text":"xlsx"}},{"id":361094,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2018/1183/ofr20181183_appendixes.zip","text":"Appendixes 1 and 2, all tables in CSV format","size":"5.45 GB","linkFileType":{"id":6,"text":"zip"}},{"id":361095,"rank":6,"type":{"id":18,"text":"Project Site"},"url":"https://webapps.usgs.gov/rsqa/#!/"},{"id":361090,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1183/coverthb.jpg"},{"id":361091,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1183/ofr20181183.pdf","text":"Report","size":"2.59 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1183"},{"id":361092,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2018/1183/ofr20181183_appendix1.xlsx","text":"Appendix 1, tables 1.1 through 1.4: Excel","size":"777 KB","linkFileType":{"id":3,"text":"xlsx"}}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.9365234375,\n 40.17887331434696\n ],\n [\n -68.291015625,\n 40.17887331434696\n ],\n [\n -68.291015625,\n 47.60616304386874\n ],\n [\n -79.9365234375,\n 47.60616304386874\n ],\n [\n -79.9365234375,\n 40.17887331434696\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
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Northborough, MA 01532