Riparian trees and their annual growth rings can be used to reconstruct drought histories related to streamflow. Because the death of individual trees reduces competition for survivors, however, tree-ring chronologies based only on surviving trees may underestimate drought impacts. This problem can be addressed by calculating productivity at the stand scale to account for tree mortality and establishment. In the semi-arid Great Basin in the western United States, we calculated riparian wood production from 1946 to 2016 along a stream where most flow has been removed by a diversion pipeline since 1961. The water table was found to be generally below the root zone of cottonwoods (Populus angustifolia and P. angustifolia × trichocarpa) in the pipeline-dewatered reach but within it in reference reaches. To reconstruct forest productivity through time, we separately combined measurements of tree-ring basal area increment with either changing forest area from aerial photos or a census of cross-dated living and dead cottonwoods. Both approaches revealed productivity declines in the dewatered reach relative to adjacent reference reaches, and the decline accelerated in the 2000s. Tree-ring narrowing resulted in divergence between the dewatered reach and one reference reach within 5 years after diversion. However, the dewatered reach did not diverge from the other reference reach until 40 years later, when an unprecedented early 2000s atmospheric drought coupled with diversion to cause extensive cottonwood mortality. We conclude that dendrochronological investigations of forest response to environmental stress should incorporate stand dynamics and that the full impacts of flow diversion can be delayed for decades.
","language":"English","publisher":"Wiley","doi":"10.1002/eco.2408","usgsCitation":"Schook, D.M., Friedman, J.M., Hoover, J.D., Rice, S.E., Thaxton, R.D., and Cooper, D.J., 2022, Riparian forest productivity decline initiated by streamflow diversion then amplified by atmospheric drought 40 years later: Ecohydrology, v. 15, no. 3, e2408, 14 p., https://doi.org/10.1002/eco.2408.","productDescription":"e2408, 14 p.","ipdsId":"IP-134136","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":448223,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eco.2408","text":"Publisher Index Page"},{"id":398106,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nevada, Utah","otherGeospatial":"Great Basin National Park, Pole Canyon, Snake Creek, Snake Range, Snake Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.36767578124999,\n 38.85815687709717\n ],\n [\n -114.13284301757812,\n 38.85815687709717\n ],\n [\n -114.13284301757812,\n 38.9396506365778\n ],\n [\n -114.36767578124999,\n 38.9396506365778\n ],\n [\n -114.36767578124999,\n 38.85815687709717\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"15","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-02-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Schook, Derek M.","contributorId":178325,"corporation":false,"usgs":false,"family":"Schook","given":"Derek","email":"","middleInitial":"M.","affiliations":[{"id":13539,"text":"Department of Geosciences, Colorado State University, Fort Collins, Colorado","active":true,"usgs":false}],"preferred":false,"id":839582,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Friedman, Jonathan M. 0000-0002-1329-0663","orcid":"https://orcid.org/0000-0002-1329-0663","contributorId":44495,"corporation":false,"usgs":true,"family":"Friedman","given":"Jonathan","middleInitial":"M.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":839583,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hoover, Jamie D.","contributorId":238180,"corporation":false,"usgs":false,"family":"Hoover","given":"Jamie","email":"","middleInitial":"D.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":839584,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rice, Steven E.","contributorId":238179,"corporation":false,"usgs":false,"family":"Rice","given":"Steven","email":"","middleInitial":"E.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":839585,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thaxton, Richard D.","contributorId":238181,"corporation":false,"usgs":false,"family":"Thaxton","given":"Richard","email":"","middleInitial":"D.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":839586,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cooper, David J.","contributorId":196510,"corporation":false,"usgs":false,"family":"Cooper","given":"David","email":"","middleInitial":"J.","affiliations":[{"id":13017,"text":"Department of Forest and Rangeland Stewardship, Colorado State University","active":true,"usgs":false}],"preferred":false,"id":839587,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70230269,"text":"70230269 - 2022 - Hydroclimatic conditions, wildfire, and species assemblages influence co-occurrence of bull trout and tailed frogs in northern Rocky Mountain streams","interactions":[],"lastModifiedDate":"2022-04-06T14:19:09.796775","indexId":"70230269","displayToPublicDate":"2022-04-05T09:14:35","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Hydroclimatic conditions, wildfire, and species assemblages influence co-occurrence of bull trout and tailed frogs in northern Rocky Mountain streams","docAbstract":"Although bull trout (Salvelinus confluentus) and tailed frogs (Ascaphus montanus) have co-existed in forested Pacific Northwest streams for millennia, these iconic cold-water specialists are experiencing rapid environmental change caused by a warming climate and enhanced wildfire activity. Our goal was to inform future conservation by examining the habitat associations of each species and conditions that facilitate co-occupancy. We repurposed data from previous studies in the northern Rocky Mountains to assess the efficacy of bull trout electrofishing surveys for determining the occurrence of tailed frogs and the predictive capacity of habitat covariates derived from in-stream measurements and geospatial sources to model distributions of both species. Electrofishing reliably detected frog presence (89.2% rate). Both species were strongly associated with stream temperature and flow regime characteristics, and less responsive to riparian canopy cover, slope, and other salmonids. Tailed frogs were also sensitive to wildfire, with occupancy probability peaking around 80 years after a fire. Co-occupancy was most probable in locations with low-to-moderate frequencies of high winter flow events, few other salmonids, a low base-flow index, and intermediate years since fire. The distributions of these species appear to be sensitive to environmental conditions that are changing this century in forests of the northern Rocky Mountains. The amplification of climate-driven effects after wildfire may prove to be particularly problematic in the future. Habitat differences between these two species, considered to be headwater specialists, suggest that conservation measures designed for one may not fully protect the other. Additional studies involving future climate and wildfire scenarios are needed to assess broader conservation strategies and the potential to identify refuge streams where both species are likely to persist, or complementary streams where each could exist separately into the future.
","language":"English","publisher":"MDPI","doi":"10.3390/w14071162","usgsCitation":"Pilliod, D., Arkle, R.S., Thurow, R.F., and Isaak, D.J., 2022, Hydroclimatic conditions, wildfire, and species assemblages influence co-occurrence of bull trout and tailed frogs in northern Rocky Mountain streams: Water, v. 14, no. 7, 1162, 20 p., https://doi.org/10.3390/w14071162.","productDescription":"1162, 20 p.","ipdsId":"IP-137594","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":448226,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w14071162","text":"Publisher Index Page"},{"id":398215,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Montana","otherGeospatial":"northern Rocky Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.20214843749999,\n 43.77109381775651\n ],\n [\n -111.26953125,\n 43.77109381775651\n ],\n [\n -111.26953125,\n 48.951366470947725\n ],\n [\n -117.20214843749999,\n 48.951366470947725\n ],\n [\n -117.20214843749999,\n 43.77109381775651\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"14","issue":"7","noUsgsAuthors":false,"publicationDate":"2022-04-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Pilliod, David S. 0000-0003-4207-3518","orcid":"https://orcid.org/0000-0003-4207-3518","contributorId":229349,"corporation":false,"usgs":true,"family":"Pilliod","given":"David S.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":839760,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Arkle, Robert S. 0000-0003-3021-1389","orcid":"https://orcid.org/0000-0003-3021-1389","contributorId":218006,"corporation":false,"usgs":true,"family":"Arkle","given":"Robert","middleInitial":"S.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":839761,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thurow, Russel F","contributorId":289775,"corporation":false,"usgs":false,"family":"Thurow","given":"Russel","email":"","middleInitial":"F","affiliations":[{"id":62244,"text":"USDA Forest Service Rocky Mountain Research Station","active":true,"usgs":false}],"preferred":false,"id":839762,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Isaak, Dan J","contributorId":289776,"corporation":false,"usgs":false,"family":"Isaak","given":"Dan","email":"","middleInitial":"J","affiliations":[{"id":62244,"text":"USDA Forest Service Rocky Mountain Research Station","active":true,"usgs":false}],"preferred":false,"id":839763,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70230444,"text":"70230444 - 2022 - Ancient winds, waves, and atmosphere in Gale Crater, Mars, inferred from sedimentary structures and wave modeling","interactions":[],"lastModifiedDate":"2022-04-26T12:21:30.20239","indexId":"70230444","displayToPublicDate":"2022-04-04T06:40:57","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5718,"text":"Journal of Geophysical Research: Planets","onlineIssn":"2169-9100","active":true,"publicationSubtype":{"id":10}},"title":"Ancient winds, waves, and atmosphere in Gale Crater, Mars, inferred from sedimentary structures and wave modeling","docAbstract":"Wave modeling and analysis of sedimentary structures were used to evaluate whether four examples of symmetrical, reversing, or straight-crested bedforms in Gale crater sandstones are preserved wave ripples; deposition by waves would demonstrate that the lake was not covered by ice at that time. Wave modeling indicates that regardless of atmospheric density, winds that exceeded the threshold of aeolian sand transport could have generated waves capable of producing nearshore wave ripples in most grain sizes of sand.
Reversing 3-m-wavelength bedforms in the Kimberley formation are interpreted not as wave ripples but rather as large aeolian ripples that formed in an atmosphere approximately as thin as at present. These exhumed bedforms define many of the ridges at outcrops that appear striated in satellite images. At Kimberley these bedforms demonstrably underlie and therefore predate subaqueous beds, suggesting that a thin atmosphere existed at least temporarily before subaqueous deposition ceased in the crater.
The other three candidate wave ripples (Square Top, Hunda, and Voe) are consistent with modeled waves, but other origins cannot be excluded. The predominance of flat-laminated (non-rippled) beds in the lacustrine Murray formation suggests that some aspect of the lake was not conducive to formation or preservation of recognizable wave ripples. Water depths may generally have been too deep, lakebed sediment may have been too fine-grained, the lake may have been smaller than modeled, or the lake may have been covered by ice.
Wave modeling and analysis of sedimentary structures were used to evaluate whether ancient lake deposits in Gale crater contain ripples formed by waves on the surface of the lake. Deposition by waves would show that the lake was not covered by ice at that time. Modeling shows that regardless of atmospheric density, winds capable of moving sand on land would generally have been strong enough to form waves that would produce ripples near shore. Large bedforms in the Kimberley formation are interpreted as ripples formed by the wind in an atmosphere similar to that of Mars today. These bedforms underlie and are older than other beds deposited in water, thereby showing that a thin atmosphere existed at least temporarily before deposition in water ceased in the crater. Three other candidate wave ripples are consistent with modeled waves, but other origins are possible. Thick sequences of sedimentary rock in Gale crater are flat-laminated rather than rippled, suggesting that some aspect of the lake was not favorable for their formation or preservation. Much of the lake may have been too deep or ice-covered, or the lake may have been smaller than modeled or had sediment too fine to form easily observed ripples.
Large-scale modelling and prediction provide insight into general influences of climate change on inland recreational fisheries; however, small-scale dynamics and local expertise will be key in developing explicit goals for managing recreational fisheries as the climate changes. The resist-accept-direct (RAD) framework encompasses the entire decision space managers consider when addressing climate influences in their local system, but to decide whether to resist, accept or direct, managers need tools to understand how specific waterbodies will be influenced by climate change. Here, a decision-support tool was developed and applied to the walleye recreational fishery in Wisconsin, USA as an example of how to link the RAD framework to real-world management of a large recreational fishery. The tool and broadscale results described here, indicating a widespread shift away from resist strategies by mid-century, can be used by managers to inform decisions about whether to resist, accept, or direct for specific walleye populations.
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III 0000-0002-1689-2133","orcid":"https://orcid.org/0000-0002-1689-2133","contributorId":189812,"corporation":false,"usgs":true,"family":"Tingley","given":"Ralph","suffix":"III","email":"","middleInitial":"W.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":846734,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Paukert, Craig P. 0000-0002-9369-8545","orcid":"https://orcid.org/0000-0002-9369-8545","contributorId":245524,"corporation":false,"usgs":true,"family":"Paukert","given":"Craig","middleInitial":"P.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":846735,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70230357,"text":"70230357 - 2022 - Evaluating temporal and spatial transferability of a tidal inundation model for foraging waterbirds","interactions":[],"lastModifiedDate":"2022-04-08T12:04:03.799162","indexId":"70230357","displayToPublicDate":"2022-04-03T07:00:17","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating temporal and spatial transferability of a tidal inundation model for foraging waterbirds","docAbstract":"For ecosystem models to be applicable outside their context of development, temporal and spatial transferability must be demonstrated. This presents a challenge for modeling intertidal ecosystems where spatiotemporal variation arises at multiple scales. Models specializing in tidal dynamics are generally inhibited from having wider ecological applications by coarse spatiotemporal resolution or high user competency. The Tidal Inundation Model of Shallow-water Availability (TiMSA) uniquely simulates tides to empirically derive a time-integrated measure of availability for a shallow-water depth range defined by the user. To evaluate temporal and spatiotemporal transferability, we employed TiMSA at the development site in the Florida Keys and at novel subsites in the Florida Bay (application site) under a different time period (application period). We used foraging little blue herons (Egretta caerulea) as the ecological unit with which to constrain the model's “water depth window,” that is, range of water depths to estimate shallow-water availability. At the development site, temporally consistent water depth windows contrasted with interannual variation in shallow-water availability, which revealed short-term changes in Little Blue Heron foraging habitat. At the application site, water depth accuracy varied by subsite and was correlated with spatial error in bathymetric elevation. Although TiMSA parameters were sensitive to environmental temporal variation and uncertainty in spatial data, a spatially explicit water depth window generated reliable estimates of shallow-water conditions over space and time at the development and application sites. By exploring the contributing factors to model error, we provide solutions to reduce uncertainty of TiMSA parameters at potential application sites and recommendations for addressing bathymetric inaccuracy in digital elevation models. Accurately quantifying spatiotemporal changes of shallow water has implications for monitoring habitat conditions for tidally influenced species and projecting future changes to coastal ecosystems in response to anthropogenic stressors and natural disturbances such as sea level rise.
The estimation and mapping of actual evapotranspiration (ETa) is an active area of applied research in the fields of agriculture and water resources. Thermal remote sensing-based methods, using coarse resolution satellites, have been successful at estimating ETa over the conterminous United States (CONUS) and other regions of the world. In this study, we present CONUS-wide ETa from Landsat thermal imagery-using the Operational Simplified Surface Energy Balance (SSEBop) model in the Google Earth Engine (GEE) cloud computing platform. Over 150,000 Landsat satellite images were used to produce 10 years of annual ETa (2010–2019) at unprecedented scale. The accuracy assessment of the SSEBop results included point-based evaluation using monthly Eddy Covariance (EC) data from 25 AmeriFlux stations as well as basin-scale comparison with annual Water Balance ETa (WBET) for more than 1000 sub-basins. Evaluations using EC data showed generally mixed performance with weaker (R2 < 0.6) correlation on sparsely vegetated surfaces such as grasslands or woody savanna and stronger correlation (R2 > 0.7) over well-vegetated surfaces such as croplands and forests, but location-specific conditions rather than cover type were attributed to the variability in accuracy. Croplands performed best with R2 of 0.82, root mean square error of 29 mm/month, and average bias of 12%. The WBET evaluation indicated that the SSEBop model is strong in explaining the spatial variability (up to R2 > 0.90) of ETa across large basins, but it also identified broad hydro-climatic regions where the SSEBop ETa showed directional biases, requiring region-specific model parameter improvement and/or bias correction with an overall 7% bias nationwide. Annual ETa anomalies over the 10-year period captured widely reported drought-affected regions, for the most part, in different parts of the CONUS, indicating their potential applications for mapping regional- and field-scale drought and fire effects. Due to the coverage of the Landsat Path/Row system, the availability of cloud-free image pixels ranged from less than 12 (mountainous cloud-prone regions and U.S. Northeast) to more than 60 (U.S. Southwest) per year. However, this study reinforces a promising application of Landsat satellite data with cloud-computing for quick and efficient mapping of ETa for agricultural and water resources assessments at the field scale.
No abstract available.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fenvs.2022.853684","usgsCitation":"Norman, L., 2022, Commentary: Dryland watershed restoration with rock detention structures: A nature-based solution to mitigate drought, erosion, flooding, and atmospheric carbon: Frontiers in Environmental Science, v. 10, 853684, 5 p., https://doi.org/10.3389/fenvs.2022.853684.","productDescription":"853684, 5 p.","ipdsId":"IP-136280","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":448283,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fenvs.2022.853684","text":"Publisher Index Page"},{"id":398112,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","state":"Arizona, Chihuahua, New Mexico, 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\"}}]}","volume":"10","noUsgsAuthors":false,"publicationDate":"2022-04-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Norman, Laura M. 0000-0002-3696-8406","orcid":"https://orcid.org/0000-0002-3696-8406","contributorId":203300,"corporation":false,"usgs":true,"family":"Norman","given":"Laura M.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":839617,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70237708,"text":"70237708 - 2022 - Fort Stanton cave science conference field guide","interactions":[],"lastModifiedDate":"2022-10-19T14:21:29.332954","indexId":"70237708","displayToPublicDate":"2022-04-01T09:08:25","publicationYear":"2022","noYear":false,"publicationType":{"id":4,"text":"Book"},"publicationSubtype":{"id":12,"text":"Conference publication"},"title":"Fort Stanton cave science conference field guide","docAbstract":"No abstract available.
","language":"English","publisher":"New Mexico Bureau of Geology and Mineral Resources","usgsCitation":"Peerman, S., Blake, J., Ferguson, C.L., Boston, P.J., Connolly, C., Miltenberger, K.E., Newton, T., and Spilde, M., 2022, Fort Stanton cave science conference field guide, 32 p.","productDescription":"32 p.","ipdsId":"IP-137324","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":408543,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":408542,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://geoinfo.nmt.edu/FtStanton/home.cfm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"New Mexico","otherGeospatial":"Fort Stanton Cave","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.5,\n 33.35\n ],\n [\n -105.7,\n 33.35\n ],\n [\n -105.7,\n 33.55\n ],\n [\n -105.5,\n 33.55\n ],\n [\n -105.5,\n 33.35\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Peerman, Steve","contributorId":298092,"corporation":false,"usgs":false,"family":"Peerman","given":"Steve","affiliations":[{"id":64495,"text":"Fort Stanton Cave Study Project","active":true,"usgs":false}],"preferred":false,"id":855094,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Blake, Johanna 0000-0003-4667-0096","orcid":"https://orcid.org/0000-0003-4667-0096","contributorId":217272,"corporation":false,"usgs":true,"family":"Blake","given":"Johanna","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":855090,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ferguson, Christina L. 0000-0003-3368-0770","orcid":"https://orcid.org/0000-0003-3368-0770","contributorId":225087,"corporation":false,"usgs":true,"family":"Ferguson","given":"Christina","email":"","middleInitial":"L.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":855091,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boston, Penelope J.","contributorId":127514,"corporation":false,"usgs":false,"family":"Boston","given":"Penelope","email":"","middleInitial":"J.","affiliations":[{"id":7026,"text":"New Mexico Tech","active":true,"usgs":false}],"preferred":false,"id":855096,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Connolly, Cynthia","contributorId":298093,"corporation":false,"usgs":false,"family":"Connolly","given":"Cynthia","email":"","affiliations":[{"id":38023,"text":"New Mexico Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":855095,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Miltenberger, K. E. 0000-0002-3874-4609","orcid":"https://orcid.org/0000-0002-3874-4609","contributorId":243647,"corporation":false,"usgs":true,"family":"Miltenberger","given":"K.","middleInitial":"E.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":855092,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Newton, Talon","contributorId":298091,"corporation":false,"usgs":false,"family":"Newton","given":"Talon","email":"","affiliations":[{"id":16150,"text":"New Mexico Bureau of Geology and Mineral Resources","active":true,"usgs":false}],"preferred":false,"id":855093,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Spilde, Mike","contributorId":298094,"corporation":false,"usgs":false,"family":"Spilde","given":"Mike","affiliations":[{"id":36307,"text":"University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":855097,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70230479,"text":"70230479 - 2022 - WaterMarks Spring 2022 Newsletter","interactions":[],"lastModifiedDate":"2022-04-14T13:17:53.528212","indexId":"70230479","displayToPublicDate":"2022-04-01T08:17:23","publicationYear":"2022","noYear":false,"publicationType":{"id":25,"text":"Newsletter"},"publicationSubtype":{"id":30,"text":"Newsletter"},"seriesTitle":{"id":10554,"text":"Watermarks New England Water Science Center Newsletter","active":true,"publicationSubtype":{"id":30}},"title":"WaterMarks Spring 2022 Newsletter","docAbstract":"No abstract available.
","language":"English","publisher":"U.S. Geological Survey","usgsCitation":"Ventetuolo, D.J., 2022, WaterMarks Spring 2022 Newsletter: Watermarks New England Water Science Center Newsletter, HTML Document.","productDescription":"HTML Document","ipdsId":"IP-139131","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":398731,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":398711,"type":{"id":15,"text":"Index Page"},"url":"https://www.usgs.gov/watermarks-new-england-wsc-newsletters/watermarks-newsletter-spring-2022"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ventetuolo, Dennis J. 0000-0002-5811-3142","orcid":"https://orcid.org/0000-0002-5811-3142","contributorId":290224,"corporation":false,"usgs":true,"family":"Ventetuolo","given":"Dennis","email":"","middleInitial":"J.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":840531,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70230703,"text":"70230703 - 2022 - To catch a (marsh) predator: Modified trapping methods for breeding and wintering Northern Harriers (Circus hudsonius)","interactions":[],"lastModifiedDate":"2022-05-13T15:09:50.467724","indexId":"70230703","displayToPublicDate":"2022-04-01T06:46:04","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2442,"text":"Journal of Raptor Research","active":true,"publicationSubtype":{"id":10}},"title":"To catch a (marsh) predator: Modified trapping methods for breeding and wintering Northern Harriers (Circus hudsonius)","docAbstract":"Although there are a variety of methods available for trapping raptors, some species, such as Northern Harriers (Circus hudsonius), are not easily captured with standard methods. We tested several existing trapping methods and modified two of them based on Northern Harrier ecology and behavior across seasons in a study population in California. No previously successful methods described in the literature were effective for our study population. Two modified methods were most effective for trapping Northern Harriers: (1) placing two dho-gazas around the nest in a V-shape and flushing the adult female into the nets during the breeding season, and (2) using remote-triggered bow nets baited with waterbird carcasses in winter. Dho-gazas at the nest worked well during the early nestling-rearing stage, when nestlings were younger than 2 wk old and adult females were most attentive. This method was not suitable during the incubation stage because Northern Harriers are prone to nest abandonment. In the winter, Northern Harriers are known to scavenge, yet this aspect of their behavioral ecology has previously been rarely exploited for trapping purposes. These two methods allow for selective Northern Harrier trapping across seasons and provide modified options for research on this understudied and declining species in North America.
Landscape-level geomorphic processes influence the spatial and temporal arrangement of fish habitats in freshwater ecosystems and fishes move across riverscapes, selecting a suite of habitats to maximise fitness. Here, we explore the influence of geomorphology on stream channel attributes and assess Broad Whitefish (Coregonus nasus) spawning habitat potential in the Colville River in Arctic Alaska. Using high-resolution digital surface models (5 m2), we quantified the stream network extent and summarised channel habitat attributes continuously across the drainage network. Next, we developed an intrinsic potential (IP) model for Broad Whitefish by using geomorphic channel parameters previously understood to be associated with spawning habitats (channel width, median substrate size and channel braiding) to estimate the potential of streams across the Colville River watershed to provide spawning habitat. Our model results show the majority of habitat with high IP (≥0.6) was located within the braided sections of the main channel, which encompass >1548 km, but only 2% of the total channel network. The IP model was tested by tracking radio-tagged Broad Whitefish using aerial surveys. Prespawn fish moved into the watershed starting mid-July and mostly used habitat with moderate to very high IP in the middle and lower watershed. Several individuals were relocated in smaller multichannels with vegetated bars that contained very low IP (≤0.2), suggesting that other factors, such as hyporheic flow, may also influence spawning habitat selection. Our study demonstrates that IP modelling offers a useful method to quantify spawning habitat potential in data-poor riverscapes, providing useful information for managers to assess potential anthropogenic impacts and develop conservation plans to protect essential Broad Whitefish habitat.
","language":"English","publisher":"Wiley","doi":"10.1111/eff.12657","usgsCitation":"Leppi, J., Falke, J.A., Rinella, D., Wipfli, M.S., Seitz, A., and Whitman, M.S., 2022, Landscape geomorphology and local-riverine features influence Broad Whitefish (Coregonus nasus) spawning habitat suitability in Arctic Alaska: Ecology of Freshwater Fish, v. 31, no. 4, p. 622-639, https://doi.org/10.1111/eff.12657.","productDescription":"18 p.","startPage":"622","endPage":"639","ipdsId":"IP-126724","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":490658,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/eff.12657","text":"Publisher Index Page"},{"id":489404,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -163.185772718535,\n 70.98799175474022\n ],\n [\n -163.185772718535,\n 66.54828717076046\n ],\n [\n -142.02222047339154,\n 66.54828717076046\n ],\n [\n -142.02222047339154,\n 70.98799175474022\n ],\n [\n -163.185772718535,\n 70.98799175474022\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"31","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-03-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Leppi, Jason C.","contributorId":338571,"corporation":false,"usgs":false,"family":"Leppi","given":"Jason C.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":938899,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Falke, Jeffrey A. 0000-0002-6670-8250 jfalke@usgs.gov","orcid":"https://orcid.org/0000-0002-6670-8250","contributorId":5195,"corporation":false,"usgs":true,"family":"Falke","given":"Jeffrey","email":"jfalke@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":938900,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rinella, Daniel J.","contributorId":355579,"corporation":false,"usgs":false,"family":"Rinella","given":"Daniel J.","affiliations":[{"id":81169,"text":"Fish and Wildlife Field Conservation Office","active":true,"usgs":false}],"preferred":false,"id":938901,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wipfli, Mark S. 0000-0002-4856-6068 mwipfli@usgs.gov","orcid":"https://orcid.org/0000-0002-4856-6068","contributorId":1425,"corporation":false,"usgs":true,"family":"Wipfli","given":"Mark","email":"mwipfli@usgs.gov","middleInitial":"S.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":938898,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Seitz, Andrew C.","contributorId":264890,"corporation":false,"usgs":false,"family":"Seitz","given":"Andrew C.","affiliations":[{"id":6695,"text":"UAF","active":true,"usgs":false}],"preferred":false,"id":938902,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Whitman, Matthew S.","contributorId":338574,"corporation":false,"usgs":false,"family":"Whitman","given":"Matthew","email":"","middleInitial":"S.","affiliations":[{"id":81170,"text":"Arctic Field Office","active":true,"usgs":false}],"preferred":false,"id":938903,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70241860,"text":"70241860 - 2022 - Resisting ecosystem transformation through an intensive whole-lake fish removal experiment","interactions":[],"lastModifiedDate":"2023-03-29T12:22:36.59024","indexId":"70241860","displayToPublicDate":"2022-03-31T07:20:40","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1659,"text":"Fisheries Management and Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Resisting ecosystem transformation through an intensive whole-lake fish removal experiment","docAbstract":"Lake ecosystems are shifting due to many drivers including climate change and landscape-scale habitat disturbance, diminishing their potential to support some fisheries. Walleye Sander vitreus (Mitchill) populations, which support recreational and tribal fisheries across North America, have declined in some lakes. Climate change, harvest, invasive species and concurrent increases in warm-water fishes (e.g. Centrarchidae) may have contributed to declines. To test the utility of an intensive management action to resist walleye loss, an experimental removal of ~285,000 centrarchids from a 33-ha lake over 4 years was conducted while monitoring the fish community response. Centrarchid abundance declined and yellow perch Perca flavescens (Mitchill) increased, yet no evidence of walleye recruitment was observed. These findings explore the feasibility of intensive resistance as a management strategy in supporting walleye facing environmental change and provide a platform for management discussions to move beyond resist strategies in the Resist-Accept-Direct (RAD) framework to navigate ecosystem change.
The U.S. Geological Survey worked in cooperation with the New York State Department of Environmental Conservation to assess the potential sources of fecal contamination entering Patchogue and Bellport Bays, two embayments on the south shore of Suffolk County, Long Island, New York. Water samples are routinely collected by the New York State Department of Environmental Conservation in the bays and analyzed for fecal coliform bacteria, an indicator of fecal contamination, to determine the need for closure of shellfish beds for harvest and consumption. Fecal coliform and other bacteria are an indicator of the potential presence of pathogenic (disease-causing) bacteria. However, indicator bacteria alone cannot determine the biological or geographical sources of contamination; therefore, microbial source tracking was implemented to determine various biological sources of contamination. In addition, information such as the location, weather and season, and surrounding land use where a sample was collected help determine the geographical source and conveyance of land-based water to the embayment.
Analysis revealed that the most substantial source of fecal contamination to Patchogue and Bellport Bays was discharge from sites draining ponds and wetlands into the rivers and tributaries sampled, particularly during the summer months. Fecal coliform bacteria at sites where ponds and wetlands drain are increased by stormwater runoff, which is another substantial source of fecal contamination. Overall, canine- and waterfowl-associated bacterial contributions were prevalent in source samples in both bays. Human-associated markers were present in surface-water source samples and completely absent in receptor samples in Patchogue Bay. The Fireplace Neck receptor site in Bellport Bay had a human-associated marker present in the summer wet sample only. Human markers were detected at the sample site downstream from the Patchogue wastewater treatment plant but were associated with low fecal coliform concentrations, indicating that the wastewater treatment plant is not a likely source of fecal contamination to Patchogue Bay. The lack of human-associated marker detections within Patchogue and Bellport Bays in summer source samples coupled with low to no detections of fecal coliform, especially where small marinas are present in creeks and tributaries, suggest that boats do not substantially contribute fecal coliform bacteria to the bays. There was little evidence of groundwater-contributing fecal bacteria by direct discharge from the subsurface. Further, the sandy sediment alongside Patchogue and Bellport Bays is unlikely to contribute fecal coliform bacteria from the test host organisms when resuspended in the water column. A classification scheme was developed to convey the degree of fecal contamination to stakeholders and resource managers. Based on this classification scheme, the Corey Creek Near Middle Road, Patchogue River Near Division Street, and Swan River Mouth sampling sites were identified as locations that contribute substantial fecal contamination to Patchogue Bay. In Bellport Bay, the Culvert at Beaverdam Creek site was identified as the location contributing the most substantial fecal contamination.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225007","collaboration":"Prepared in cooperation with New York State Department of Environmental Conservation","usgsCitation":"Tagliaferri, T.N., Fisher, S.C., Kephart, C.M., Cheung, N., Reed, A.P., and Welk, R.J., 2022, Using microbial source tracking to identify fecal contamination sources in Patchogue and Bellport Bays on Long Island, New York: U.S. Geological Survey Scientific Investigations Report 2022-5007, 30 p., https://doi.org/10.3133/sir20225007.","productDescription":"Report: vii, 30 p.; Database","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-129363","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":397779,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5007/coverthb.jpg"},{"id":397780,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5007/sir20225007.pdf","text":"Report","size":"1.81 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5007"},{"id":397781,"rank":3,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the nation"},{"id":397782,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5007/sir20225007.XML"},{"id":397783,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5007/images/"},{"id":397854,"rank":6,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.er.usgs.gov/publication/sir20215033","text":"Scientific Investigations Report 2021–5033","linkHelpText":"- Overview and Methodology for a Study To Identify Fecal Contamination Sources Using Microbial Source Tracking in Seven Embayments on Long Island, New York"},{"id":397884,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225007/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":502295,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112752.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"New York","otherGeospatial":"Long Island, Patchogue Bay, Bellport Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.0810546875,\n 40.706148461723764\n ],\n [\n -72.83592224121094,\n 40.706148461723764\n ],\n [\n -72.83592224121094,\n 40.795617968801466\n ],\n [\n -73.0810546875,\n 40.795617968801466\n ],\n [\n -73.0810546875,\n 40.706148461723764\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180-8349
The U.S. Geological Survey, in cooperation with the Ashokan Watershed Stream Management Program, investigated the feasibility of bedload monitoring in the upper Esopus Creek watershed, Ulster and Greene Counties, New York, from 2017 to 2020. Traditional bedload samples were collected at two locations: Birch Creek at Big Indian, New York (station 013621955), and Stony Clove Creek at Jansen Road at Lanesville, New York (station 01362336), during two storms. Measured bedload-transport rates ranged from less than 1 to 37.2 short tons per day during the study period. Active and passive tracers were deployed in Stony Clove Creek at Jansen Road to measure bed-material displacement during storms. Accelerometers in the active tracers provided data on the initiation and duration of motion of bed material in the 128- to 190-millimeter size class (B-axis measurement of 175 millimeters). The active tracers were loosely placed on the streambed and were generally mobilized at streamflows of 130–375 cubic feet per second. Displacement of the passive tracers was measured five times and provided data on the variability of displacement of multiple-size classes of bed material by different streamflows. Passive-tracer data also indicated that particles larger than the opening of the Elwha sampler deployed for traditional sampling may have been in transport during sampling. Sediment-generated noise was not distinguishable from background stream noise in hydrophone recordings.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1151","collaboration":"Prepared in cooperation with the Ashokan Watershed Stream Management Program","usgsCitation":"Siemion, J., Antidormi, M.R., Bonville, D.B., Finkelstein, J., and Marineau, M., 2022, Bed-material transport in the upper Esopus Creek watershed, Ulster and Greene Counties, New York, 2017–20: U.S. Geological Survey Data Report 1151, 20 p., https://doi.org/10.3133/dr1151.","productDescription":"Report: vi, 20 p.; Data Release","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-130089","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":397770,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1151/coverthb.jpg"},{"id":397771,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1151/dr1151.pdf","text":"Report","size":"3.84 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New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
The U.S. Geological Survey (USGS) calculated discharge at 13 water control structures in Florida using theoretical equations and uncalibrated coefficients gathered from previous studies and typical textbook values for selected flow regimes and structure types. These discharges were compared to the real-time discharges calculated and published by the USGS from October 1, 2007, to September 30, 2019, using traditional methods and coefficients verified by direct discharge measurements. The theoretical and USGS-calculated daily mean discharges were compared at each structure for different flow regimes covering the entire range of discharges that occurred over the study period except those less than 10 cubic feet per second to avoid large percentage errors for small actual differences in discharge. The discharges were also not compared if (1) any alterations were made to the USGS discharge to account for factors such as debris or construction, (2) any values were missing throughout the day, (3) the flow regime changed during the day, or (4) the USGS discharge was estimated. The structures compared include a mixture of vertical lift and radial gates with free and submerged conditions for orifice and weir flow.
The study totals showed that the average absolute difference for all structures was 18.7 percent. Average percent differences ranged from −26.5 to 28.6 percent, and 4 of the 13 structures had average differences within 10 percent.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221011","usgsCitation":"Ryan, P.J., and Hazelbaker, C.L., 2022, Comparison of computed flow through manually operated water control structures in Florida using theoretical versus calibrated coefficients: U.S. Geological Survey Open-File Report 2022–1011, 25 p., https://doi.org/10.3133/ofr20221011.","productDescription":"Report: vii, 25 p.; Data Release; Dataset","numberOfPages":"38","onlineOnly":"Y","ipdsId":"IP-129610","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":501757,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112754.htm","linkFileType":{"id":5,"text":"html"}},{"id":397688,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1011/coverthb.jpg"},{"id":397689,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1011/ofr20221011.pdf","text":"Report","size":"3.35 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1011"},{"id":397691,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1011/ofr20221011.XML"},{"id":397692,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1011/images"},{"id":397693,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MHF0GD","text":"USGS data release","linkHelpText":"Data for the comparison of computed flow through manually operated water control structures in Florida using theoretical versus calibrated coefficients"},{"id":397695,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":397885,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221011/full","text":"Report","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.9248046875,\n 28.70986084394286\n ],\n [\n -83.1884765625,\n 28.110748760633534\n ],\n [\n -82.33154296875,\n 26.03704188651584\n ],\n [\n -81.23291015625,\n 25.443274612305746\n ],\n [\n -80.13427734374999,\n 25.383735254706842\n ],\n [\n -79.73876953125,\n 26.725986812271756\n ],\n [\n -80.22216796875,\n 28.323724553546015\n ],\n [\n -81.03515625,\n 29.496987596535742\n ],\n [\n -83.38623046875,\n 29.49698759653577\n ],\n [\n -82.9248046875,\n 28.70986084394286\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
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Marine protected area (MPA) designs, including large-scale MPAs (LSMPAs; >150,000 km2), mobile MPAs (fluid spatiotemporal boundaries), and MPA networks, may offer different benefits to species and could enhance protection by encompassing spatiotemporal scales of animal movement. We sought to understand how well LSMPAs could benefit nine highly-mobile marine species in the tropics now and into the future by: 1) evaluating current range overlap within a LSMPA; 2) evaluating range overlap under climate change projections; and 3) evaluating how well theoretical MPA designs benefit these nine species. We focused on Palmyra Atoll and Kingman Reef, a 2000 km2 area within the 1.2 million km2 U.S. Pacific Remote Islands Marine National Monument (PRIMNM) that contains marine megafauna (reef and pelagic fishes; sea turtles; seabirds; cetaceans) reflecting different behaviors and habitat use. Our approach is useful for evaluating the effectiveness of the Palmyra-Kingman MPA and PRIMNM in protecting these species, and tropical LSMPAs in general, and for informing future MPA design. Stationary MPAs provided protection at varying scales. Reef manta rays (Mobula alfredi), grey reef sharks (Carcharhinus amblyrhynchos), green sea turtles (Chelonia mydas), and bottlenose dolphins (Tursiops truncatus) had overall small ranges (<100 km from Palmyra-Kingman) and could benefit from stationary MPAs that contained heterogenous reef habitats. Yellowfin tuna (Thunnus albacares), sooty terns (Onychoprion fuscatus), red-footed boobies (Sula sula), great frigatebirds (Fregata minor), and melon-headed whales (Peponocephala electra) navigated complex oceanographic processes and may benefit most from mobile MPAs that shift with features including thermal fronts, cyclic regions of elevated productivity, and eddies, if relationships with these features are established and predictable. All species had capacity to travel to nearby reef systems, illustrating potential benefits of MPA networks and protected corridors. Suitable habitats will likely contract for all species as warm water expands under climate change scenarios (species habitats were predicted to decrease by 4–49% at Palmyra-Kingman) and MPAs may not protect suitable habitats into the future. Species habitat requirements and movement ecologies are critical aspects of marine spatial planning, especially with respect to dynamic ocean processes and a changing climate.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2022.e02070","usgsCitation":"Gilmour, M.E., Adams, J., Block, B.A., Caselle, J.E., Friedlander, A.M., Game, E.T., Hazen, E.L., Holmes, N.D., Lafferty, K.D., Maxwell, S.M., McCauley, D., Oleson, E.M., Pollock, K.H., Shaffer, S.A., Wolff, N.H., and Wegmann, A., 2022, Evaluation of MPA designs that protect highly mobile megafauna now and under climate change scenarios: Global Ecology and Conservation, v. 35, e02070, 19 p., https://doi.org/10.1016/j.gecco.2022.e02070.","productDescription":"e02070, 19 p.","ipdsId":"IP-134828","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":448316,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2022.e02070","text":"Publisher Index Page"},{"id":397860,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Fiji, Samoa, United States, Vanuatu","state":"Hawai'i","otherGeospatial":"Baker Island, Howland Island, Jarvis Island, Johnson Atoll, Kingman Reef, Line Islands, Pacific Remote Islands Marine National Monument, Palmyra Atoll, Solomon Islands, Wake Atoll","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -206.015625,\n -18.895892559415024\n ],\n [\n -152.05078125,\n -18.895892559415024\n ],\n [\n -152.05078125,\n 23.644524198573688\n ],\n [\n -206.015625,\n 23.644524198573688\n ],\n [\n -206.015625,\n -18.895892559415024\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"35","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gilmour, Morgan Elizabeth 0000-0002-2618-1095","orcid":"https://orcid.org/0000-0002-2618-1095","contributorId":289509,"corporation":false,"usgs":true,"family":"Gilmour","given":"Morgan","email":"","middleInitial":"Elizabeth","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":839201,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":839202,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Block, Barbara A.","contributorId":150815,"corporation":false,"usgs":false,"family":"Block","given":"Barbara","email":"","middleInitial":"A.","affiliations":[{"id":18108,"text":"Tuna Research and Conservation Center, Stanford University, Hopkins Marine Station, Pacific Grove, California 93950, U.S.A","active":true,"usgs":false}],"preferred":false,"id":839203,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Caselle, Jennifer E.","contributorId":127450,"corporation":false,"usgs":false,"family":"Caselle","given":"Jennifer","email":"","middleInitial":"E.","affiliations":[{"id":6710,"text":"University of California, Santa Barbara, CA","active":true,"usgs":false}],"preferred":false,"id":839204,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Friedlander, A. 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M.","contributorId":289520,"corporation":false,"usgs":false,"family":"Oleson","given":"E.","email":"","middleInitial":"M.","affiliations":[{"id":62166,"text":"Pacific Islands Fisheries Science Center, National Oceanic and Atmospheric Administration, Honolulu, HI 96818, USA","active":true,"usgs":false}],"preferred":false,"id":839212,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Pollock, Kenneth H.","contributorId":8590,"corporation":false,"usgs":false,"family":"Pollock","given":"Kenneth","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":839213,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Shaffer, S. A.","contributorId":207689,"corporation":false,"usgs":false,"family":"Shaffer","given":"S.","email":"","middleInitial":"A.","affiliations":[{"id":24620,"text":"San Jose State University","active":true,"usgs":false}],"preferred":false,"id":839214,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Wolff, N. H.","contributorId":289522,"corporation":false,"usgs":false,"family":"Wolff","given":"N.","email":"","middleInitial":"H.","affiliations":[{"id":62168,"text":"The Nature Conservancy, Brunswick, ME 04011, USA","active":true,"usgs":false}],"preferred":false,"id":839215,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Wegmann, Alex","contributorId":189488,"corporation":false,"usgs":false,"family":"Wegmann","given":"Alex","email":"","affiliations":[],"preferred":false,"id":839216,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}} ,{"id":70230196,"text":"70230196 - 2022 - A biological condition gradient for coral reefs in the US Caribbean Territories: Part I. Coral narrative rules","interactions":[],"lastModifiedDate":"2022-04-04T16:49:10.741224","indexId":"70230196","displayToPublicDate":"2022-03-29T11:42:14","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"A biological condition gradient for coral reefs in the US Caribbean Territories: Part I. Coral narrative rules","docAbstract":"As coral reef condition and sustainability continue to decline worldwide, losses of critical habitat and their ecosystem services have generated an urgency to understand and communicate reef response to management actions, environmental contamination, and natural disasters. Increasingly, coral reef protection and restoration programs emphasize the need for robust assessment tools for protecting high-quality waters and establishing conservation goals. Of equal importance is the need to communicate assessment results to stakeholders, beneficiaries, and the public so that environmental consequences of decisions are understood. The Biological Condition (BCG) model provides a structure to evaluate the condition of a coral reef in increments of change along a gradient of human disturbance. Communication of incremental change, regardless of direction, is important for decision makers and the public to better understand what is gained or lost depending on what actions are taken. We developed a narrative (qualitative) Biological Condition Gradient (BCG) from the consensus of a diverse expert panel to provide a framework for coral reefs in US Caribbean Territories. The model uses narrative descriptions of biological attributes for benthic organisms to evaluate reefs relative to undisturbed or minimally disturbed conditions. Using expert elicitation, narrative decision rules were proposed and deliberated to discriminate among six levels of change along a gradient of increasing anthropogenic stress. Narrative rules for each of the BCG levels are presented to facilitate the evaluation of benthic communities in coral reefs and provide specific narrative features to detect changes in coral reef condition and biological integrity. The BCG model can be used in the absence of numeric, or quantitative metrics, to evaluate actions that may encroach on coral reef ecosystems, manage endangered species habitat, and develop and implement management plans for marine protected areas, watersheds, and coastal zones. The narrative BCG model is a defensible model and communication tool that translates scientific results so the nontechnical person can understand and support both regulatory and non-regulatory water quality and natural resource programs.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2022.108805","usgsCitation":"Santavy, D.L., Jackson, S.K., Jessup, B., Gerritsen, J., Rogers, C., Fisher, W.S., Weil, E., Szmant, A., Cuevas-Miranda, D., Walker, B.K., Jeffrey, C., Bradley, P., Ballantine, D., Roberson, L., Ruiz-Torres, H., Todd, B., Smith, T.B., Clark, R., Diaz, E.L., Bauza-Ortega, J., Horstmann, C., and Raimondo, S., 2022, A biological condition gradient for coral reefs in the US Caribbean Territories: Part I. Coral narrative rules: Ecological Indicators, v. 138, 108805, 13 p., https://doi.org/10.1016/j.ecolind.2022.108805.","productDescription":"108805, 13 p.","ipdsId":"IP-132623","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":448332,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2022.108805","text":"Publisher Index Page"},{"id":398022,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Puerto Rico, U.S. Virgin Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -68.236083984375,\n 17.38209494787749\n ],\n [\n -64.544677734375,\n 17.38209494787749\n ],\n [\n -64.544677734375,\n 18.646245142670608\n ],\n [\n 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L.","contributorId":212197,"corporation":false,"usgs":false,"family":"Diaz","given":"Ernesto","email":"","middleInitial":"L.","affiliations":[{"id":38453,"text":"Department of Natural and Environmental Resources, Coastal Zone Management Program","active":true,"usgs":false}],"preferred":false,"id":839502,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Bauza-Ortega, Jorge","contributorId":289624,"corporation":false,"usgs":false,"family":"Bauza-Ortega","given":"Jorge","email":"","affiliations":[{"id":62202,"text":"San Juan Bay National Estuary Program","active":true,"usgs":false}],"preferred":false,"id":839503,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Horstmann, Christina","contributorId":272093,"corporation":false,"usgs":false,"family":"Horstmann","given":"Christina","email":"","affiliations":[{"id":56350,"text":"Oak Ridge Institute for Science Education Participant at US 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Toxicology","active":true,"publicationSubtype":{"id":10}},"title":"Chloride toxicity to native freshwater species in natural and reconstituted prairie pothole waters","docAbstract":"Oil and gas extraction in the Prairie Pothole Region (PPR) of the northern USA has resulted in elevated chloride concentrations in ground and surface water due to widespread contamination with highly saline produced water, or brine. The toxicity of chloride is poorly understood in the high hardness waters characteristic of the region. We evaluated the toxicity of chloride to two endemic species, Daphnia magna (water flea) and Lemna gibba (duckweed), exposed in field-collected waters (hardness ~ 3000 mg/L as CaCO3) and reconstituted waters (hardness 370 mg/L as CaCO3) intended to mimic PPR background waters. We also investigated the role of chloride in the toxicity of water reconstituted to mimic legacy brine-contaminated wetlands, using two populations of native Pseudacris maculata (Boreal Chorus Frog). Chloride toxicity was similar in field-collected and reconstituted waters for both D. magna (LC50s 3070–3788 mg Cl−1/L) and L. gibba (IC50s 2441–2887). Although hardness can ameliorate chloride toxicity at low to high hardness, we did not observe additional protection as hardness increased from 370 to ~ 3000 mg/L. In P. maculata exposures, chloride did not fully explain toxicity. Chloride sensitivity also differed between populations, with mortality at 2000 mg Cl−/L in one population but not the other, and population-specific growth responses. Overall, these results (1) document toxicity to native species at chloride concentrations occurring in the PPR, (2) indicate that very high hardness in the region’s waters may not provide additional protection against chloride and (3) highlight challenges of brine investigations, including whether surrogate study populations are representative of local populations.
","language":"English","publisher":"Springer","doi":"10.1007/s00244-022-00927-6","usgsCitation":"Harper, D., Puglis, H.J., Kunz, B.K., and Farag, A., 2022, Chloride toxicity to native freshwater species in natural and reconstituted prairie pothole waters: Archives of Environmental Contamination and Toxicology, v. 82, no. 3, p. 416-428, https://doi.org/10.1007/s00244-022-00927-6.","productDescription":"13 p.","startPage":"416","endPage":"428","ipdsId":"IP-134067","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":435903,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BQ6YRZ","text":"USGS data release","linkHelpText":"Biological and chemical data from chloride bioassays with native wetland species in natural and reconstituted Prairie Pothole waters"},{"id":412680,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri, Montana, North Dakota, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -95.85320647480594,\n 48.99581509747179\n ],\n [\n -97.77592768425632,\n 48.99581509747179\n ],\n [\n -97.77592768425632,\n 48.223855848161435\n ],\n [\n -95.85320647480594,\n 48.223855848161435\n ],\n [\n -95.85320647480594,\n 48.99581509747179\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -110.64129262095554,\n 43.90595317724575\n ],\n [\n -110.64129262095554,\n 43.79956999926114\n ],\n [\n -110.49449847140379,\n 43.79956999926114\n ],\n [\n -110.49449847140379,\n 43.90595317724575\n ],\n [\n -110.64129262095554,\n 43.90595317724575\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -92.8164783846994,\n 40.361202906330504\n ],\n [\n -92.93357933722913,\n 40.361202906330504\n ],\n [\n -92.93357933722913,\n 40.26768330837069\n ],\n [\n -92.8164783846994,\n 40.26768330837069\n ],\n [\n -92.8164783846994,\n 40.361202906330504\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"82","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-03-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Harper, David 0000-0001-7061-8461 david_harper@usgs.gov","orcid":"https://orcid.org/0000-0001-7061-8461","contributorId":169848,"corporation":false,"usgs":true,"family":"Harper","given":"David","email":"david_harper@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":863225,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Puglis, Holly J. 0000-0002-3090-6597 hpuglis@usgs.gov","orcid":"https://orcid.org/0000-0002-3090-6597","contributorId":4686,"corporation":false,"usgs":true,"family":"Puglis","given":"Holly","email":"hpuglis@usgs.gov","middleInitial":"J.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":863226,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kunz, Bethany K. 0000-0002-7193-9336 bkunz@usgs.gov","orcid":"https://orcid.org/0000-0002-7193-9336","contributorId":3798,"corporation":false,"usgs":true,"family":"Kunz","given":"Bethany","email":"bkunz@usgs.gov","middleInitial":"K.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":863227,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Farag, Aida 0000-0003-4247-6763 aida_farag@usgs.gov","orcid":"https://orcid.org/0000-0003-4247-6763","contributorId":200690,"corporation":false,"usgs":true,"family":"Farag","given":"Aida","email":"aida_farag@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":863228,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70230841,"text":"70230841 - 2022 - Development and description of a composite hydrogeologic framework for inclusion in a geoenvironmental assessment of undiscovered uranium resources in Pliocene- to Pleistocene-age geologic units of the Texas Coastal Plain","interactions":[],"lastModifiedDate":"2022-04-26T14:00:19.627318","indexId":"70230841","displayToPublicDate":"2022-03-29T08:51:44","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5207,"text":"Minerals","active":true,"publicationSubtype":{"id":10}},"title":"Development and description of a composite hydrogeologic framework for inclusion in a geoenvironmental assessment of undiscovered uranium resources in Pliocene- to Pleistocene-age geologic units of the Texas Coastal Plain","docAbstract":"A previously completed mineral resources assessment of the Texas Coastal Plain indicated the potential for the future discovery of uranium resources. Geoenvironmental assessments that include the hydrogeologic framework can be used as a tool to understand the potential effects of mining operations. The hydrogeologic framework for this study focused on the composite hydrogeologic unit of the tract permissive for the occurrence of uranium consisting of the upper part of the Miocene-age Fleming Formation/Lagarto Clay, Pliocene-age Goliad and Pleistocene-age Willis Sands, Pleistocene-age Lissie and Beaumont Formations, and Holocene-age alluvial sediments (fluvial alluvium and eolian sand deposits). This composite hydrogeologic unit, which contains the Chicot and Evangeline aquifers of the Gulf Coast aquifer system, is intended for inclusion in a regional-scale geoenvironmental assessment of as yet undiscovered uranium resources. This article provides (1) a brief literature review describing the geologic and hydrogeologic settings, (2) the methodology used to develop a composite hydrogeologic framework, and (3) descriptions and maps of the land-surface altitude, composite hydrogeologic unit base and midpoint depth, water-level altitude, depth of water, unsaturated and saturated zone thickness, and transmissivity and hydraulic conductivity. A composite hydrogeologic unit, created by combining geologic and hydrogeologic data and maps for individual geologic and hydrogeologic units, is intended for use as a tool in a geoenvironmental assessment to evaluate potential contaminant migration through various avenues. Potential applications include using the hydrogeologic framework as an input into a geoenvironmental assessment to help estimate the potential for (1) runoff of contaminants into surface water, (2) infiltration of contaminants into the groundwater (aquifers), or (3) movement of contaminants from the mining area through wind, groundwater-flow, or streamflow in a given permissive tract. The procedures outlined in this paper also provide a method for developing hydrogeologic frameworks that can be applied in other areas where mining may occur.
","language":"English","publisher":"MDPI","doi":"10.3390/min12040420","usgsCitation":"Teeple, A., Becher, K.D., Walton-Day, K., Humberson, D.G., and Gallegos, T., 2022, Development and description of a composite hydrogeologic framework for inclusion in a geoenvironmental assessment of undiscovered uranium resources in Pliocene- to Pleistocene-age geologic units of the Texas Coastal Plain: Minerals, v. 12, no. 4, 420, 29 p., https://doi.org/10.3390/min12040420.","productDescription":"420, 29 p.","ipdsId":"IP-136336","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":448338,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/min12040420","text":"Publisher Index Page"},{"id":435904,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AZMEU0","text":"USGS data release","linkHelpText":"Data used for developing a composite hydrogeologic framework for inclusion in a geoenvironmental assessment of undiscovered uranium resources in Pliocene- to Pleistocene-age geologic units of the Texas Coastal Plain"},{"id":399663,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.84521484375,\n 29.707139348134145\n ],\n [\n -93.69140625,\n 30.29701788337205\n ],\n [\n -93.55957031249999,\n 30.996445897426373\n ],\n [\n -93.84521484375,\n 31.74685416292141\n ],\n [\n -94.0869140625,\n 32.287132632616384\n ],\n [\n -94.19677734375,\n 32.97180377635759\n ],\n [\n -95.537109375,\n 32.62087018318113\n ],\n [\n -96.7236328125,\n 30.86451022625836\n ],\n [\n -97.9541015625,\n 29.34387539941801\n ],\n [\n -99.11865234374999,\n 27.994401411046148\n ],\n [\n -99.36035156249999,\n 27.332735136859146\n ],\n [\n -99.29443359375,\n 26.745610382199022\n ],\n [\n -98.67919921875,\n 26.254009699865737\n ],\n [\n -98.06396484375,\n 26.03704188651584\n ],\n [\n -97.42675781249999,\n 25.958044673317843\n ],\n [\n -97.00927734375,\n 25.760319754713887\n ],\n [\n -97.1630859375,\n 26.54922257769204\n ],\n [\n -97.1630859375,\n 27.547241546253268\n ],\n [\n -95.8447265625,\n 28.5941685062326\n ],\n [\n -94.41650390625,\n 29.305561325527698\n ],\n [\n -93.84521484375,\n 29.707139348134145\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-03-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Teeple, Andrew 0000-0003-1781-8354 apteeple@usgs.gov","orcid":"https://orcid.org/0000-0003-1781-8354","contributorId":193061,"corporation":false,"usgs":true,"family":"Teeple","given":"Andrew","email":"apteeple@usgs.gov","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":841445,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Becher, Kent D 0000-0002-3947-0793","orcid":"https://orcid.org/0000-0002-3947-0793","contributorId":290642,"corporation":false,"usgs":false,"family":"Becher","given":"Kent","email":"","middleInitial":"D","affiliations":[{"id":37374,"text":"Retired USGS","active":true,"usgs":false}],"preferred":false,"id":841446,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walton-Day, Katherine 0000-0002-9146-6193 kwaltond@usgs.gov","orcid":"https://orcid.org/0000-0002-9146-6193","contributorId":184043,"corporation":false,"usgs":true,"family":"Walton-Day","given":"Katherine","email":"kwaltond@usgs.gov","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":841447,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Humberson, Delbert G 0000-0001-6789-9135","orcid":"https://orcid.org/0000-0001-6789-9135","contributorId":240891,"corporation":false,"usgs":false,"family":"Humberson","given":"Delbert","email":"","middleInitial":"G","affiliations":[],"preferred":false,"id":841448,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gallegos, Tanya J. 0000-0003-3350-6473","orcid":"https://orcid.org/0000-0003-3350-6473","contributorId":206859,"corporation":false,"usgs":true,"family":"Gallegos","given":"Tanya J.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":841449,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70230714,"text":"70230714 - 2022 - Can machine learning accelerate process understanding and decision-relevant predictions of river water quality?","interactions":[],"lastModifiedDate":"2022-05-13T15:19:01.582298","indexId":"70230714","displayToPublicDate":"2022-03-29T06:42:16","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Can machine learning accelerate process understanding and decision-relevant predictions of river water quality?","docAbstract":"The global decline of water quality in rivers and streams has resulted in a pressing need to design new watershed management strategies. Water quality can be affected by multiple stressors including population growth, land use change, global warming, and extreme events, with repercussions on human and ecosystem health. A scientific understanding of factors affecting riverine water quality and predictions at local to regional scales, and at sub-daily to decadal timescales are needed for optimal management of watersheds and river basins. Here, we discuss how machine learning (ML) can enable development of more accurate, computationally tractable, and scalable models for analysis and predictions of river water quality. We review relevant state-of-the art applications of ML for water quality models and discuss opportunities to improve the use of ML for emerging computational and mathematical methods for model selection, hyperparameter optimization, incorporating process knowledge into ML models, improving explainablity, uncertainty quantification, and model-data integration. We then present considerations for using ML to address water quality problems given their scale and complexity, available data and computational resources, and stakeholder needs. When combined with decades of process understanding, interdisciplinary advances in knowledge-guided ML, information theory, data integration, and analytics can help address fundamental science questions and enable decision-relevant predictions of riverine water quality.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.14565","usgsCitation":"Varadharajan, C., Appling, A.P., Arora, B., Christianson, D., Hendrix, V., Kumar, V., Lima, A.R., Mueller, J., Oliver, S.K., Ombadi, M., Perciano, T., Sadler, J.M., Weierbach, H., Willard, J., Xu, Z., and Zwart, J.A., 2022, Can machine learning accelerate process understanding and decision-relevant predictions of river water quality?: Hydrological Processes, v. 36, e14565, 22 p., https://doi.org/10.1002/hyp.14565.","productDescription":"e14565, 22 p.","ipdsId":"IP-133065","costCenters":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":448340,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.14565","text":"Publisher Index Page"},{"id":399487,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"36","noUsgsAuthors":false,"publicationDate":"2022-04-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Varadharajan, Charuleka","contributorId":242712,"corporation":false,"usgs":false,"family":"Varadharajan","given":"Charuleka","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":841217,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Appling, Alison P. 0000-0003-3638-8572 aappling@usgs.gov","orcid":"https://orcid.org/0000-0003-3638-8572","contributorId":150595,"corporation":false,"usgs":true,"family":"Appling","given":"Alison","email":"aappling@usgs.gov","middleInitial":"P.","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":841218,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Arora, Bhavna 0000-0001-7841-886X","orcid":"https://orcid.org/0000-0001-7841-886X","contributorId":290532,"corporation":false,"usgs":false,"family":"Arora","given":"Bhavna","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":841219,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Christianson, Danielle","contributorId":265829,"corporation":false,"usgs":false,"family":"Christianson","given":"Danielle","email":"","affiliations":[{"id":39617,"text":"Lawrence Berkeley National Lab","active":true,"usgs":false}],"preferred":false,"id":841220,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hendrix, Valerie 0000-0001-9061-8952","orcid":"https://orcid.org/0000-0001-9061-8952","contributorId":290533,"corporation":false,"usgs":false,"family":"Hendrix","given":"Valerie","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":841221,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kumar, Vipin","contributorId":237812,"corporation":false,"usgs":false,"family":"Kumar","given":"Vipin","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":841222,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lima, Aranildo R.","contributorId":290536,"corporation":false,"usgs":false,"family":"Lima","given":"Aranildo","email":"","middleInitial":"R.","affiliations":[{"id":25337,"text":"Aquatic Informatics","active":true,"usgs":false}],"preferred":false,"id":841223,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mueller, Juliane 0000-0001-8627-1992","orcid":"https://orcid.org/0000-0001-8627-1992","contributorId":290539,"corporation":false,"usgs":false,"family":"Mueller","given":"Juliane","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":841224,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Oliver, Samantha K. 0000-0001-5668-1165","orcid":"https://orcid.org/0000-0001-5668-1165","contributorId":211886,"corporation":false,"usgs":true,"family":"Oliver","given":"Samantha","email":"","middleInitial":"K.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":841225,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Ombadi, Mohammed","contributorId":290542,"corporation":false,"usgs":false,"family":"Ombadi","given":"Mohammed","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":841226,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Perciano, Talita 0000-0002-2388-1803","orcid":"https://orcid.org/0000-0002-2388-1803","contributorId":290546,"corporation":false,"usgs":false,"family":"Perciano","given":"Talita","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":841227,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Sadler, Jeffrey Michael 0000-0001-8776-4844","orcid":"https://orcid.org/0000-0001-8776-4844","contributorId":260092,"corporation":false,"usgs":true,"family":"Sadler","given":"Jeffrey","email":"","middleInitial":"Michael","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":841228,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Weierbach, Helen","contributorId":290549,"corporation":false,"usgs":false,"family":"Weierbach","given":"Helen","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":841229,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Willard, Jared","contributorId":237808,"corporation":false,"usgs":false,"family":"Willard","given":"Jared","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":841230,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Xu, Zexuan","contributorId":290552,"corporation":false,"usgs":false,"family":"Xu","given":"Zexuan","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":841231,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Zwart, Jacob Aaron 0000-0002-3870-405X","orcid":"https://orcid.org/0000-0002-3870-405X","contributorId":237809,"corporation":false,"usgs":true,"family":"Zwart","given":"Jacob","email":"","middleInitial":"Aaron","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":841232,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}} ,{"id":70230092,"text":"ofr20221007 - 2022 - Uncertainty analysis of index-velocity meters and discharge computations at the Chicago Sanitary and Ship Canal near Lemont, Illinois, water years 2006–16","interactions":[],"lastModifiedDate":"2026-03-27T19:43:51.579154","indexId":"ofr20221007","displayToPublicDate":"2022-03-28T13:10:41","publicationYear":"2022","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":"2022-1007","displayTitle":"Uncertainty Analysis of Index-Velocity Meters and Discharge Computations at the Chicago Sanitary and Ship Canal near Lemont, Illinois, Water Years 2006–16","title":"Uncertainty analysis of index-velocity meters and discharge computations at the Chicago Sanitary and Ship Canal near Lemont, Illinois, water years 2006–16","docAbstract":"Monitoring discharge in the Chicago Sanitary and Ship Canal is critical for the accounting done by the U.S. Army Corps of Engineers of the diversion of water from Lake Michigan to the Mississippi River Basin by the State of Illinois. The primary streamgage used for this discharge monitoring, the Chicago Sanitary and Ship Canal near Lemont, Illinois (U.S. Geological Survey station 05536890), is operated by the U.S. Geological Survey as an index-velocity station and at the time of this study (water years 2006–16) had two continuous velocity meters (an acoustic Doppler velocity meter and an acoustic velocity meter) and a water-level sensor, among other instruments. Discharge is computed at the streamgage using an index-velocity rating developed by linear regression of the velocity meter values fitted to discharges intermittently measured with an acoustic Doppler current profiler. In this study, the uncertainties of the velocity meters and stage sensors were estimated using a type B (judgment-based) approach, and measured discharge uncertainties were taken from those provided by a common acoustic Doppler current profiler data processing software tool, QRev. The velocity meter uncertainties, expressed as standard deviations, were estimated to be about 2.5 percent of velocity except near zero, where they exceeded that fraction, whereas for the acoustic Doppler current profiler uncertainties, when converted to mean channel velocity, 2.5 percent of velocity was determined to be a lower bound. The estimated velocity meter and measured discharge uncertainties were compared to index-velocity ratings developed from regression analyses of two types: (1) those that allow specification of measurement uncertainties and (2) ordinary least squares (OLS) regression, which does not. Based on the linearity of the index-velocity rating and the approximate agreement of the distributions of the fitting and prediction velocities, the assumptions required for unbiased prediction by OLS regression were determined to be approximately satisfied. From the regression residuals, it was determined that the estimated measurement uncertainties are too small, too similar between acoustic velocity meter and acoustic Doppler velocity meter velocities, and possibly too strongly dependent on velocity. Large, non-Gaussian OLS regression residuals also were observed. The uncertainty of annual mean discharge computed using the different regressions also was considered and was determined to be strongly dependent on the assumed measurement uncertainty. Because the assumptions required for OLS regression to give unbiased and variance-maintaining predictions were determined to be approximately satisfied, the results of discharge computation using the index-velocity rating based on OLS regression were deemed to be reliable. These results indicate about 0.8-percent uncertainty in the computed discharge as measured by the coefficient of variation at the annual time scale when using the acoustic Doppler velocity meter and 1.2-percent uncertainty with the acoustic velocity meter. It may be possible to improve the accuracy of the computed discharge and its uncertainty by further examining the measurement uncertainties and addressing differences in the distributions of the velocities used in fitting the index-velocity ratings and those used in prediction. Although the index-velocity ratings and computed discharges presented in this study are similar to those used in computing the published discharge at the study streamgage, the values presented in this report are not intended to replace the published discharge.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221007","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers, Chicago District","usgsCitation":"Over, T.M., Muste, M., Duncker, J.J., Tsai, H., Jackson, P.R., Johnson, K.K., Engel, F.L., and Prater, C.D., 2022, Uncertainty analysis of index-velocity meters and discharge computations at the Chicago Sanitary and Ship Canal near Lemont, Illinois, water years 2006–16: U.S. Geological Survey Open-File Report 2022–1007, 35 p., https://doi.org/10.3133/ofr20221007.","productDescription":"Report: viii, 35 p.; Appendix; Data Release; Dataset","numberOfPages":"48","onlineOnly":"Y","ipdsId":"IP-125889","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":501754,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112755.htm","linkFileType":{"id":5,"text":"html"}},{"id":397713,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7X63K41","text":"USGS data release","linkHelpText":"Discharge measurements at U.S. Geological Survey streamgage 05536890 Chicago Sanitary and Ship Canal near Lemont, Illinois, 2005–2013"},{"id":397709,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2022/1007/ofr20221007_appendix2.pdf","text":"Appendix 2","size":"7.03 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1007 appendix 2","linkHelpText":"—Slides"},{"id":397714,"rank":7,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":397708,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1007/ofr20221007.pdf","text":"Report","size":"13.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1007"},{"id":397712,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1007/images"},{"id":397711,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1007/ofr20221007.XML"},{"id":397707,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1007/coverthb.jpg"}],"country":"United States","state":"Illinois","city":"Lemont","otherGeospatial":"Chicago Sanitary and Ship Canal","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.06846618652344,\n 41.66573093599398\n ],\n [\n -88.04718017578125,\n 41.64469659784919\n ],\n [\n -87.86796569824217,\n 41.699063978799174\n ],\n [\n -87.75672912597656,\n 41.789744876718984\n ],\n [\n -87.7979278564453,\n 41.83068856472101\n ],\n [\n -87.92701721191406,\n 41.75645886225854\n ],\n [\n -88.06709289550781,\n 41.6908605241911\n ],\n [\n -88.06846618652344,\n 41.66573093599398\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
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Urbana, IL 61801