Conventional, field-based streamflow monitoring in remote, inaccessible locations such as Alaska poses logistical challenges. Safety concerns, financial considerations, and a desire to expand water-observing networks make remote sensing an appealing alternative means of collecting hydrologic data. In an ongoing effort to develop non-contact methods for measuring river discharge, we evaluated the potential to estimate surface flow velocities from satellite video of a large, sediment-laden river in Alaska via particle image velocimetry (PIV). In this setting, naturally occurring sediment boil vortices produced distinct water surface features that could be tracked from frame to frame as they were advected by the flow, obviating the need to introduce artificial tracer particles. In this study, we refined an end-to-end workflow that involved stabilization and geo-referencing, image preprocessing, PIV analysis with an ensemble correlation algorithm, and post-processing of PIV output to filter outliers and scale and geo-reference velocity vectors. Applying these procedures to image sequences extracted from satellite video allowed us to produce high resolution surface velocity fields; field measurements of depth-averaged flow velocity were used to assess accuracy. Our results confirmed the importance of preprocessing images to enhance contrast and indicated that lower frame rates (e.g., 0.25 Hz) lead to more reliable velocity estimates because longer capture intervals allow more time for water surface features to translate several pixels between frames, given the relatively coarse spatial resolution of the satellite data. Although agreement between PIV-derived velocity estimates and field measurements was weak (R2 = 0.39) on a point-by-point basis, correspondence improved when the PIV output was aggregated to the cross-sectional scale. For example, the correspondence between cross-sectional maximum velocities inferred via remote sensing and measured in the field was much stronger (R2 = 0.76), suggesting that satellite video could play a role in measuring river discharge. Examining correlation matrices produced as an intermediate output of the PIV algorithm yielded insight on the interactions between image frame rate and sensor spatial resolution, which must be considered in tandem. Although further research and technological development are needed, measuring surface flow velocities from satellite video could become a viable tool for streamflow monitoring in certain fluvial environments.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/frwa.2021.652213","usgsCitation":"Legleiter, C.J., and Kinzel, P.J., 2021, Surface flow velocities from space: Particle image velocimetry of satellite video of a large, sediment-laden river: Frontiers in Water, v. 3, 652213, 20 p., https://doi.org/10.3389/frwa.2021.652213.","productDescription":"652213, 20 p.","ipdsId":"IP-125455","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":452077,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/frwa.2021.652213","text":"Publisher Index Page"},{"id":436332,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZY5LK1","text":"USGS data release","linkHelpText":"Satellite video and field measurements of flow velocity acquired from the Tanana River in Alaska and used for particle image velocimetry (PIV)"},{"id":386020,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","city":"Nenana","otherGeospatial":"Tanana River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -149.21218872070312,\n 64.53486288126804\n ],\n [\n -148.92929077148438,\n 64.53486288126804\n ],\n [\n -148.92929077148438,\n 64.61387025268262\n ],\n [\n -149.21218872070312,\n 64.61387025268262\n ],\n [\n -149.21218872070312,\n 64.53486288126804\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"3","noUsgsAuthors":false,"publicationDate":"2021-05-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Legleiter, Carl J. 0000-0003-0940-8013 cjl@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-8013","contributorId":169002,"corporation":false,"usgs":true,"family":"Legleiter","given":"Carl","email":"cjl@usgs.gov","middleInitial":"J.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":816651,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kinzel, Paul J. 0000-0002-6076-9730 pjkinzel@usgs.gov","orcid":"https://orcid.org/0000-0002-6076-9730","contributorId":743,"corporation":false,"usgs":true,"family":"Kinzel","given":"Paul","email":"pjkinzel@usgs.gov","middleInitial":"J.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":816652,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70220871,"text":"sir20205057 - 2021 - Flood-inundation maps for the Blue River near Red Bridge Road, Kansas City, Missouri, 2019","interactions":[],"lastModifiedDate":"2021-05-28T19:21:03.271116","indexId":"sir20205057","displayToPublicDate":"2021-05-28T11:11:37","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5057","displayTitle":"Flood-Inundation Maps for the Blue River near Red Bridge Road, Kansas City, Missouri, 2019","title":"Flood-inundation maps for the Blue River near Red Bridge Road, Kansas City, Missouri, 2019","docAbstract":"Digital flood-inundation maps for a 4.6-mile reach of the Blue River near Red Bridge Road in Kansas City, Missouri, were created by the U.S. Geological Survey (USGS), in cooperation with the City of Kansas City, Missouri. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Program website at https://www.usgs.gov/mission-areas/water-resources/science/flood-inundation-mapping-fim-program, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage 06893195, Blue River at Red Bridge Road, Kansas City, Mo. Near-real-time stages at this streamgage may be obtained from the USGS National Water Information System at https://doi.org/10.5066/F7P55KJN or the Johnson County, Kansas, StormWatch Automated Local Elevation in Real Time Flood Warning System at https://www.stormwatch.com.
Flood profiles were computed for the Blue River reach by means of a one-dimensional model for simulating water-surface profiles with steady-state flow computations. The model was calibrated by using the current stage-streamflow relations at the upstream USGS streamgage 06893150, Blue River at Blue Ridge Boulevard Extension, Kansas City, Mo., and the downstream streamgage 06893500, Blue River at Kansas City, Mo.
The hydraulic model was then used to compute 37 water-surface profiles for flood stages at 1-foot (ft) intervals referenced to the streamgage datum and ranging from 11 ft, or near bankfull, to 47 ft at the reference streamgage 06893195. The upper stage for the map library exceeds the stage corresponding to the estimated 0.2-percent annual exceedance probability flood (500-year recurrence interval flood) in the model reach. The simulated water-surface profiles were then combined with a geographic information system digital elevation model with a maximum 10-centimeter vertical root mean square error and 4.0-ft horizontal resolution to delineate the area flooded at each water level.
The availability of these maps, along with real-time internet information regarding current stage from the USGS streamgage, will help guide emergency management personnel and residents in flood mitigation, preparedness and planning, flood-response activities such as evacuations and road closures, and any postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205057","collaboration":"Prepared in cooperation with the City of Kansas City, Missouri","usgsCitation":"Heimann, D.C., Voss, J.D., and Rydlund, P.H., Jr., 2021, Flood-inundation maps for the Blue River near Red Bridge Road, Kansas City, Missouri, 2019: U.S. Geological Survey Scientific Investigations Report 2020–5057, 14 p., https://doi.org/10.3133/sir20205057.","productDescription":"Report: vi, 14 p.; Data Release; Dataset","numberOfPages":"24","onlineOnly":"Y","ipdsId":"IP-117597","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":385983,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90MH291","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Geospatial datasets for the flood-inundation study of the Blue River near Red Bridge Road, Kansas City, Missouri, 2019"},{"id":385984,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System","description":"USGS Dataset","linkHelpText":"— USGS water data for the Nation"},{"id":385981,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5057/coverthb.jpg"},{"id":385982,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5057/sir20205057.pdf","text":"Report","size":"1.51 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5057"}],"country":"United States","state":"Kansas, Missouri","otherGeospatial":"Blue River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.83333,\n 38.8333\n ],\n [\n -94.45,\n 38.8333\n ],\n [\n -94.45,\n 39.1666\n ],\n [\n -94.833333,\n 39.1666\n ],\n [\n -94.833333,\n 38.8333\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
The Souris River Basin is a 61,000-square-kilometer basin in the Provinces of Saskatchewan and Manitoba in Canada and the State of North Dakota in the United States. Greater than average snowpack during the winter of 2010–11, along with record-setting rains in May and June 2011, resulted in historically unprecedented flooding in the Souris River Basin. The severity of the 2011 flood led the United States and Canada to request a review of the operating plan for any improvements of reservoir operations and flood control measures in the basin, and the Souris River Basin Task Force was formed. The International Souris River Study Board was then formed in 2017 to carry out the recommendations of the Souris River Basin Task Force laid out in a plan of study. To support the International Souris River Study Board, the U.S. Geological Survey (USGS), in cooperation with the North Dakota State Water Commission and the International Joint Commission, used the previously developed unregulated and regulated streamflow models and data for stochastic streamflow in the Souris River Basin to characterize climate and streamflow and support selection of streamflow traces based on their characterization. Components of the original stochastic hydrology models and their outputs were used in this phase of the study to (1) characterize historical and stochastic climate and streamflow for the Souris River Basin, (2) disaggregate monthly stochastic streamflow spatially and temporally to meet the needs of the U.S. Army Corps of Engineers, Hydrologic Engineering Center, Reservoir System Simulation model for the Souris River Basin, and (3) discuss selection of disaggregated streamflow traces (simulations) using the characteristics of climate and streamflow. A trace is a time series of a stochastic variable such as streamflow, potential evapotranspiration, or precipitation.
To characterize climate conditions, precipitation, potential evapotranspiration (PET), and moisture deficit for the Souris River Basin and individual points at Rafferty, Grant Devine, and Lake Darling Reservoirs were determined annually and seasonally. The annual basin (November 1–October 31) precipitation for the 50-percent nonexceedance probability is 452 millimeters (mm). Spring (March–May) is the wettest season, followed by summer (June–August), fall (September–November), and winter (December–February). Annual moisture deficit was largest at Lake Darling Reservoir, followed by Rafferty Reservoir, and then Grant Devine Reservoir.
Annual maximum monthly mean streamflow was determined for the Souris River below Rafferty Reservoir, Saskatchewan (Canadian streamgage 05NB036); Long Creek near Noonan (above Boundary Reservoir), North Dakota (USGS streamgage 05113600); Moose Mountain Creek near Oxbow, Saskatchewan (Canadian streamgage 05ND004); the Souris River near Sherwood, N. Dak. (USGS streamgage 05114000); the Des Lacs River at Foxholm, N. Dak. (USGS streamgage 05116500); and the Souris River above Minot, N. Dak. (USGS streamgage 05117500). When the seasonal maximum monthly mean streamflows are evaluated in contrast to annual maximum monthly mean streamflows separated by their seasonal occurrence, summer months of annual maximum monthly mean streamflows have a higher 50-percent exceedance probability of streamflow compared to annual maximum monthly mean streamflows that occur in spring, seasonal maximum monthly mean streamflows that occur in spring, and seasonal maximum monthly mean streamflows that occur in summer. When annual maximum monthly mean streamflows in summer are compared to annual maximum monthly mean streamflows in spring, they are consistently higher in streamflow but occur in less than 4.2 percent of years. Evaluation of whether the annual maximum monthly mean streamflows that occur in summer can be described as a separate population from annual maximum monthly mean streamflows that occur in spring was outside the scope of this study, and the summer and spring annual maximum monthly mean streamflows were not tested for statistical differences in mean or variance. Further investigation of seasonal weather patterns that induce flooding could lead to a better understanding of the seasonal differences in flooding.
Long-term hydrologic drought was characterized by evaluating multiyear mean streamflow. Shorter averaging periods have greater streamflow variability than longer periods and hence have a wider range of values. As the averaging period is extended to a longer period, the variability of mean streamflow decreases, and the more extreme streamflow volumes seen in shorter averaging periods cannot be sustained. Stochastic streamflow time series were disaggregated spatially and temporally for use in a HEC–ResSim model. The combination of monthly and daily stochastic streamflow data was used to select traces with qualities that could be used to test alternatives focused on water supply, summer flooding, and apportionment.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215044","collaboration":"Prepared in cooperation with the North Dakota State Water Commission and the International Joint Commission","usgsCitation":"Gregory, A., and Galloway, J.M., 2021, Characterization of historical and stochastically generated climate and streamflow conditions in the Souris River Basin, United States and Canada: U.S. Geological Survey Scientific Investigations Report 2021–5044, 36 p., https://doi.org/10.3133/sir20215044.","productDescription":"Report: viii, 36 p.; Data Release; Dataset","numberOfPages":"48","onlineOnly":"Y","ipdsId":"IP-120682","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":386014,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","linkHelpText":"— USGS water data for the Nation"},{"id":386011,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5044/coverthb.jpg"},{"id":386012,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5044/sir20215044.pdf","text":"Report","size":"5.03 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021—5044"},{"id":386013,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93AOWFL","text":"USGS data release","linkHelpText":"Historical and stochastically generated climate and streamflow data for the Souris River Basin, United States and Canada"}],"country":"Canada, United States","state":"Manitoba, North Dakota, Saskatchewan","otherGeospatial":"Souris River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.55859375,\n 46.6795944656402\n ],\n [\n -98.0859375,\n 50.12057809796008\n ],\n [\n -101.25,\n 51.67255514839674\n ],\n [\n -107.138671875,\n 53.48804553605622\n ],\n [\n -108.6328125,\n 50.958426723359935\n ],\n [\n -102.568359375,\n 48.22467264956519\n ],\n [\n -99.66796875,\n 46.98025235521883\n ],\n [\n -97.55859375,\n 46.6795944656402\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503
1608 Mountain View Road
Rapid City, SD 57702
Martian surface conditions are cold and dry, unfavorable for liquid water, yet steep slopes display young and currently active features suggestive of wet processes. These include recurring slope lineae and slope streaks, gully landforms, and small lobate features. Wet origins for these features would imply surprising amounts of liquid water at the surface. However, detailed observations of the morphology and activity of these features have demonstrated that dry processes, some of them unique to the Martian environment, can account for all of them. This reconciles the contradiction between physics and geomorphology and provides a self-consistent model of a Martian surface that is very active today despite having negligible volumes of liquid water.
In Humboldt Bay, tectonic subsidence exacerbates sea-level rise (SLR). To build surface elevations and to keep pace with SLR, the sediment demand created by subsidence and SLR must be balanced by an adequate sediment supply. This study used an ensemble of plausible future scenarios to predict potential climate change impacts on suspended-sediment discharge (Qss) from fluvial sources. Streamflow was simulated using a deterministic water-balance model, and Qss was computed using statistical sediment-transport models. Changes relative to a baseline period (1981–2010) were used to assess climate impacts. For local basins that discharge directly to the bay, the ensemble means projected increases in Qss of 27% for the mid-century (2040–2069) and 58% for the end-of-century (2070–2099). For the Eel River, a regional sediment source that discharges sediment-laden plumes to the coastal margin, the ensemble means projected increases in Qss of 53% for the mid-century and 99% for the end-of-century. Climate projections of increased precipitation and streamflow produced amplified increases in the regional sediment supply that may partially or wholly mitigate sediment demand caused by the combined effects of subsidence and SLR. This finding has important implications for coastal resiliency. Coastal regions with an increasing sediment supply may be more resilient to SLR. In a broader context, an increasing sediment supply from fluvial sources has global relevance for communities threatened by SLR that are increasingly building resiliency to SLR using sediment-based solutions that include regional sediment management, beneficial reuse strategies, and marsh restoration.
","language":"English","publisher":"Springer","doi":"10.1007/s12237-021-00938-x","usgsCitation":"Curtis, J., Flint, L.E., Stern, M.A., Lewis, J., and Klein, R.D., 2021, Amplified impact of climate change on fine-sediment delivery to a subsiding coast, Humboldt Bay, California: Estuaries and Coasts, v. 44, p. 2173-2193, https://doi.org/10.1007/s12237-021-00938-x.","productDescription":"21 p.","startPage":"2173","endPage":"2193","ipdsId":"IP-102755","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":452090,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s12237-021-00938-x","text":"Publisher Index Page"},{"id":436333,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97UBENK","text":"USGS data release","linkHelpText":"Daily Basin Characterization Model (BCM) archive for Humboldt Bay/Eel River"},{"id":386195,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"Humboldt County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.49707031249999,\n 39.53793974517623\n ],\n [\n -123.96972656249999,\n 39.53793974517623\n ],\n [\n -123.96972656249999,\n 41.41801503608022\n ],\n [\n -124.49707031249999,\n 41.41801503608022\n ],\n [\n -124.49707031249999,\n 39.53793974517623\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"44","noUsgsAuthors":false,"publicationDate":"2021-05-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Curtis, Jennifer 0000-0001-7766-994X","orcid":"https://orcid.org/0000-0001-7766-994X","contributorId":212727,"corporation":false,"usgs":true,"family":"Curtis","given":"Jennifer","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816912,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Flint, Lorraine E. 0000-0002-7868-441X lflint@usgs.gov","orcid":"https://orcid.org/0000-0002-7868-441X","contributorId":1184,"corporation":false,"usgs":true,"family":"Flint","given":"Lorraine","email":"lflint@usgs.gov","middleInitial":"E.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816913,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stern, Michelle A. 0000-0003-3030-7065 mstern@usgs.gov","orcid":"https://orcid.org/0000-0003-3030-7065","contributorId":4244,"corporation":false,"usgs":true,"family":"Stern","given":"Michelle","email":"mstern@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816914,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lewis, Jack","contributorId":189105,"corporation":false,"usgs":false,"family":"Lewis","given":"Jack","email":"","affiliations":[],"preferred":false,"id":816915,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Klein, Randy D.","contributorId":259269,"corporation":false,"usgs":false,"family":"Klein","given":"Randy","email":"","middleInitial":"D.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":816916,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70221343,"text":"70221343 - 2021 - Use of the smeltCam as an efficient fish sampling alternative within the San Francisco Estuary","interactions":[],"lastModifiedDate":"2021-06-11T12:05:22.631939","indexId":"70221343","displayToPublicDate":"2021-05-28T07:04:41","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3331,"text":"San Francisco Estuary and Watershed Science","active":true,"publicationSubtype":{"id":10}},"title":"Use of the smeltCam as an efficient fish sampling alternative within the San Francisco Estuary","docAbstract":"Resource managers often rely on long-term monitoring surveys to detect trends in biological data. However, no survey gear is 100% efficient, and many sources of bias can be responsible for detecting or not detecting biological trends. The SmeltCam is an imaging apparatus developed as a potential sampling alternative to long-term trawling gear surveys within the San Francisco Estuary, California, to reduce handling stress on sensitive species like the Delta Smelt (Hypomesus transpacificus). Although believed to be a reliable alternative to closed cod-end trawling surveys, no formal test of sampling efficiency has been implemented using the SmeltCam. We used a paired deployment of the SmeltCam and a conventional closed cod-end trawl within the Napa River and San Pablo Bay, a Bayesian binomial N-mixture model, and data simulations to determine the sampling efficiency of both deployed gear types to capture a Delta Smelt surrogate (Northern Anchovy, Engraulis mordax) and to test potential bias in our modeling framework. We found that retention efficiency—a component of detection efficiency that estimates the probability a fish is retained by the gear, conditional on gear contact—was slightly higher using the SmeltCam (mean = 0.58) than the conventional trawl (mean = 0.47, Probability SmeltCam retention efficiency > trawl retention efficiency = 94%). We also found turbidity did not affect the SmeltCam’s retention efficiency, although total fish density during an individual tow improved the trawl’s retention efficiency. Simulations also showed the binomial model was accurate when model assumptions were met. Collectively, our results suggest the SmeltCam to be a reliable alternative to sampling with conventional trawling gear, but future tests are needed to confirm whether the SmeltCam is as reliable when applied to taxa other than Northern Anchovy over a greater range of conditions.
","language":"English","publisher":"University of California","doi":"10.15447/sfews.2021v19iss2art6","usgsCitation":"Huntsman, B., Feyrer, F.V., and Young, M.J., 2021, Use of the smeltCam as an efficient fish sampling alternative within the San Francisco Estuary: San Francisco Estuary and Watershed Science, v. 19, no. 2, 16 p., https://doi.org/10.15447/sfews.2021v19iss2art6.","productDescription":"16 p.","ipdsId":"IP-123894","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":452096,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.15447/sfews.2021v19iss2art6","text":"Publisher Index Page"},{"id":386410,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"San Francisco","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.57421875,\n 36.84446074079564\n ],\n [\n -121.86035156249999,\n 36.84446074079564\n ],\n [\n -121.86035156249999,\n 39.40224434029275\n ],\n [\n -123.57421875,\n 39.40224434029275\n ],\n [\n -123.57421875,\n 36.84446074079564\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"19","issue":"2","noUsgsAuthors":false,"publicationDate":"2020-06-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Huntsman, Brock 0000-0003-4090-1949","orcid":"https://orcid.org/0000-0003-4090-1949","contributorId":223101,"corporation":false,"usgs":true,"family":"Huntsman","given":"Brock","email":"","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":817384,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Feyrer, Frederick V. 0000-0003-1253-2349 ffeyrer@usgs.gov","orcid":"https://orcid.org/0000-0003-1253-2349","contributorId":178379,"corporation":false,"usgs":true,"family":"Feyrer","given":"Frederick","email":"ffeyrer@usgs.gov","middleInitial":"V.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":817385,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Young, Matthew J. 0000-0001-9306-6866 mjyoung@usgs.gov","orcid":"https://orcid.org/0000-0001-9306-6866","contributorId":206255,"corporation":false,"usgs":true,"family":"Young","given":"Matthew","email":"mjyoung@usgs.gov","middleInitial":"J.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":817386,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70239051,"text":"70239051 - 2021 - Predicting light regime controls on primary productivity across CONUS river networks","interactions":[],"lastModifiedDate":"2022-12-22T13:03:46.202793","indexId":"70239051","displayToPublicDate":"2021-05-28T06:54:43","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Predicting light regime controls on primary productivity across CONUS river networks","docAbstract":"Solar radiation is a fundamental driver of ecosystem productivity, but widespread estimates of light available for primary producers in rivers are lacking. We developed a model to predict light available for river primary producers and used it to estimate river primary production across the contiguous United States (CONUS). Successively accounting for riparian and water column processes improved predictions of primary production as a function of light. We calculated the ratio of river width to riparian tree height and used this metric to predict whether riparian zones or water column processes most limit productivity for over 2 million reaches. Water column processes limited productivity for 50% of the nation's river length and 80% of its surface area, with variations across ecoregions related to riparian forest cover. Our findings facilitate large-scale predictions of stream and river ecosystem productivity, as well as understanding the processes controlling productivity across networks.
The topography of Mt. Etna, Italy, is subjected to continuous modifications depending on intensity and magnitude of eruptions that frequently occur at the volcano summit and flanks. In order to make high-resolution maps of morphological changes and accurately calculate the overall volume of the erupted products (e.g., lava flows, tephra fall out, scoriae cones) in ten years, we have compared the altimetry models of Mt. Etna derived from 2005 Airborne Laser Scanning data and 2015 Pleiades stereo satellite imagery. Both models cover a common area of 400 km2 with spatial resolution of 2 m and comparable vertical accuracy (RMSE < 0.8 m). The results show that the area most affected by the erupted products is the mid-upper portion of the volcano with an altitude ranging from 1300 m to more than 3300 m a.s.l., value reached at the summit of the North East crater. In particular, this portion changes dramatically in the eastern sector due to the birth and growth of the New South-East Crater, the invasion of dozens of lava flows in the Valle del Bove, and the formation of the 2014 scoriae cones and lava field at the base of the North-East Crater. The total volume of products erupted in the investigated period results in 284.3±15.8 x 106 m3 with a yearly average volume of 28.4 x 106 m3/y comparable with the previous decades. In addition, the products emitted by the 2014 sub-terminal eruption are mapped and quantified including, for the first time, the volume of the 2014 scoriae cones generated on the eastern flank of North-East Crater This study demonstrates how a rigorous comparison between digital elevation models derived from different remote sensing techniques produce high accurate mapping and quantifications of morphological changes applicable for worldwide active volcanoes. This allows to quantify volumes and areas of erupted products reducing the error estimations, a crucial point to provide precise data often used as key parameters for many volcanic hazard studies.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jag.2021.102369","usgsCitation":"Bisson, M., Spinetti, C., Andronico, D., Palaseanu-Lovejoy, M., Buongiorno, M.F., Alexandrov, O., and Cecere, T., 2021, Ten years of volcanic activity at Mt Etna: High-resolution mapping and accurate quantification of the morphological changes by Pleiades and Lidar data: International Journal of Applied Earth Observations and Geoinformation, v. 102, 102369, 11 p., https://doi.org/10.1016/j.jag.2021.102369.","productDescription":"102369, 11 p.","ipdsId":"IP-121404","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":452102,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jag.2021.102369","text":"Publisher Index Page"},{"id":386026,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Italy","otherGeospatial":"Mt. Etna, Sicily","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 14.849395751953125,\n 37.62402129571883\n ],\n [\n 15.137786865234377,\n 37.62402129571883\n ],\n [\n 15.137786865234377,\n 37.85859141570558\n ],\n [\n 14.849395751953125,\n 37.85859141570558\n ],\n [\n 14.849395751953125,\n 37.62402129571883\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"102","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bisson, Marina 0000-0002-7104-9210","orcid":"https://orcid.org/0000-0002-7104-9210","contributorId":221724,"corporation":false,"usgs":false,"family":"Bisson","given":"Marina","email":"","affiliations":[{"id":40408,"text":"Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Pisa, via Della Faggiola, Pisa, 56126, Italy","active":true,"usgs":false}],"preferred":false,"id":816657,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Spinetti, Claudia 0000-0002-1861-5666","orcid":"https://orcid.org/0000-0002-1861-5666","contributorId":221725,"corporation":false,"usgs":false,"family":"Spinetti","given":"Claudia","email":"","affiliations":[{"id":40409,"text":"Istituto Nazionale di Geofisica e Vulcanologia, Sezione ONT, via di Vigna Murata, Roma, 00143, Italy","active":true,"usgs":false}],"preferred":false,"id":816658,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Andronico, Daniele 0000-0002-8333-1547","orcid":"https://orcid.org/0000-0002-8333-1547","contributorId":259163,"corporation":false,"usgs":false,"family":"Andronico","given":"Daniele","email":"","affiliations":[{"id":52323,"text":"Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Etneo","active":true,"usgs":false}],"preferred":false,"id":816659,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Palaseanu-Lovejoy, Monica 0000-0002-3786-5118 mpal@usgs.gov","orcid":"https://orcid.org/0000-0002-3786-5118","contributorId":3639,"corporation":false,"usgs":true,"family":"Palaseanu-Lovejoy","given":"Monica","email":"mpal@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true}],"preferred":true,"id":816660,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Buongiorno, Maria Fabrizia 0000-0002-6095-6974","orcid":"https://orcid.org/0000-0002-6095-6974","contributorId":221726,"corporation":false,"usgs":false,"family":"Buongiorno","given":"Maria","email":"","middleInitial":"Fabrizia","affiliations":[{"id":40409,"text":"Istituto Nazionale di Geofisica e Vulcanologia, Sezione ONT, via di Vigna Murata, Roma, 00143, Italy","active":true,"usgs":false}],"preferred":false,"id":816661,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Alexandrov, Oleg","contributorId":167662,"corporation":false,"usgs":false,"family":"Alexandrov","given":"Oleg","email":"","affiliations":[{"id":24796,"text":"NASA Ames Research Center","active":true,"usgs":false}],"preferred":false,"id":816662,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cecere, Thomas 0000-0001-5254-8404 tcecere@usgs.gov","orcid":"https://orcid.org/0000-0001-5254-8404","contributorId":221727,"corporation":false,"usgs":true,"family":"Cecere","given":"Thomas","email":"tcecere@usgs.gov","affiliations":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":true,"id":816663,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70229737,"text":"70229737 - 2021 - Improving short-term recruitment forecasts for coho salmon using a spatiotemporal integrated population model","interactions":[],"lastModifiedDate":"2022-03-16T16:11:47.02032","indexId":"70229737","displayToPublicDate":"2021-05-27T11:06:46","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1661,"text":"Fisheries Research","active":true,"publicationSubtype":{"id":10}},"title":"Improving short-term recruitment forecasts for coho salmon using a spatiotemporal integrated population model","docAbstract":"Fishery managers often rely on forecasts of future population abundance to set allowable harvest quotas or exploitation rates. While there has been substantial research devoted to identifying environmental factors that can predict recruitment for individual populations, such correlations often degrade over time, thereby limiting their utility for management. Conversely, examining multiple populations at once to detect shared, spatially structured patterns can offer insights into their recruitment dynamics that are advantageous for forecasting. Here, we develop a population dynamics model for natural origin coho salmon (Oncorhynchus kisutch) stocks in Washington State that leverages spatial and temporal autocorrelation in marine survival to improve one-year-ahead forecasts of adult returns. Executed in a Bayesian hierarchical integrated modelling framework, our spatiotemporal approach incorporates multiple data types and shares information among stocks to estimate key biological parameters that are informative for forecasting. Retrospective evaluation of one-year-ahead forecast skill indicated that the spatiotemporal integrated population model (ST-IPM) outperformed existing forecasts of Washington State coho salmon returns by 25–38 % on average. Moreover, the ST-IPM estimates parameters that were previously non-identifiable for many stocks, and propagates uncertainty from multiple contributing data sources into model forecasts. Our results add to a growing body of work demonstrating the utility of spatiotemporal and integrated approaches for modelling population dynamics, and the framework developed here has broad applications to the assessment and management of coho salmon in Washington State and elsewhere throughout their range.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.fishres.2021.106014","usgsCitation":"DeFilippo, L.B., Buehrens, T., Scheuerell, M.D., Kendall, N.W., and Schindler, D.E., 2021, Improving short-term recruitment forecasts for coho salmon using a spatiotemporal integrated population model: Fisheries Research, v. 242, 106014, 12 p., https://doi.org/10.1016/j.fishres.2021.106014.","productDescription":"106014, 12 p.","ipdsId":"IP-129173","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":452108,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.fishres.2021.106014","text":"Publisher Index Page"},{"id":397172,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"242","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"DeFilippo, Lukas B.","contributorId":288622,"corporation":false,"usgs":false,"family":"DeFilippo","given":"Lukas","email":"","middleInitial":"B.","affiliations":[{"id":12729,"text":"UW","active":true,"usgs":false}],"preferred":false,"id":838141,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Buehrens, Thomas W.","contributorId":288623,"corporation":false,"usgs":false,"family":"Buehrens","given":"Thomas W.","affiliations":[{"id":12729,"text":"UW","active":true,"usgs":false}],"preferred":false,"id":838142,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Scheuerell, Mark David 0000-0002-8284-1254","orcid":"https://orcid.org/0000-0002-8284-1254","contributorId":288621,"corporation":false,"usgs":true,"family":"Scheuerell","given":"Mark","email":"","middleInitial":"David","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":838140,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kendall, Neala W.","contributorId":288624,"corporation":false,"usgs":false,"family":"Kendall","given":"Neala","email":"","middleInitial":"W.","affiliations":[{"id":61815,"text":"wafg","active":true,"usgs":false}],"preferred":false,"id":838143,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schindler, Daniel E.","contributorId":288625,"corporation":false,"usgs":false,"family":"Schindler","given":"Daniel","email":"","middleInitial":"E.","affiliations":[{"id":12729,"text":"UW","active":true,"usgs":false}],"preferred":false,"id":838144,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70220879,"text":"70220879 - 2021 - Appendix C: Central sands lakes study technical report: Modeling documentation","interactions":[],"lastModifiedDate":"2021-05-27T14:04:05.646141","indexId":"70220879","displayToPublicDate":"2021-05-27T08:51:14","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":8761,"text":"Wisconsin DNR Technical Report","active":true,"publicationSubtype":{"id":2}},"title":"Appendix C: Central sands lakes study technical report: Modeling documentation","docAbstract":"This report provides the necessary documentation of the numerical models developed for the Central Sands Lake study in central Wisconsin and will be included as a technical appendix in the report to the Wisconsin State Legislature by the Wisconsin Department of Natural Resources (WDNR) in response to 2017 Wisconsin Act 10. This legislation directed WDNR to determine whether existing and potential groundwater withdrawals are causing or are likely to cause significant reduction of mean seasonal water levels at Pleasant Lake, Long Lake, and Plainfield Lake (s. 281.34(7m)(2)(b), Wis. Stats.) in Waushara County, Wisconsin. To evaluate the potential hydrologic connection between groundwater withdrawals and the nearby study lakes, hydrologic models were created that focused on the lakes of interest and yet were large enough to cover a broad enough region to extend to the major hydrologic boundaries of the natural flow system. The areas near the lakes require finer-scale grid discretization (or spacing) to better represent the lakes and streams in the model, but also need to cover a large enough area to include the groundwater withdrawal locations that have the potential to cause reduction in water levels in the lakes. To accomplish these goals, three groundwater models were created: a regional model extending to major hydrologic boundaries; and two inset models, inheriting boundaries from the regional model but focused near the lakes. Each of the inset models, in turn, included a detailed area close to the lakes surrounded by an area at the same spatial scale as the regional model (Figure 1).
To support WDNR in evaluating the connection between groundwater withdrawals and lake levels, a representative time period was required over which to compare land use with and without irrigated agriculture and for WDNR to evaluate potential lake stage and flux changes related to irrigated agriculture. WDNR chose the climate period of 1981-2018 to be representative of a typical period and provided two land use scenarios—one with no irrigated agriculture and one with assumed crop rotations similar to current conditions—to simulate with groundwater models to, then, compare lake responses with. As a result, simulations over this climate record are not intended to recreate the history of 1981-2018 because land use changed over that time. These runs are, instead, intended to provide a basis on which to compare land use with and without irrigation-related groundwater withdrawals based on the current arrangement of land use and a varied climatic record. Groundwater withdrawals focused on irrigated-agriculture-related water use because greater than 95% of groundwater withdrawal in the two inset models around the study lakes is for irrigated agriculture water use.
The period of 2012-2018 was used for parameter estimation (synonymously referred to as “history matching”) for the groundwater models. This time period was chosen because it includes the most complete water use records to simulate groundwater withdrawals. History matching was performed using groundwater elevations, lake stages, and streamflow observations over the 2012-2018 time period and processed observations derived from those raw data.
Climatic data were incorporated into the model using a soil-water balance approach. A soil water balance model was constructed at the scale of the regional groundwater model to both calculate recharge based on land use and climate, and in the long-term climate-period runs, to estimate water use required by irrigated agriculture to apply as well boundary conditions in the groundwater model in the absence of reported water use values over that period.
","language":"English","publisher":"Wisconsin Department of Natural Resources","usgsCitation":"Fienen, M., Haserodt, M.J., Leaf, A.T., and Westenbroek, S., 2021, Appendix C: Central sands lakes study technical report: Modeling documentation: Wisconsin DNR Technical Report, ix, 137 p.","productDescription":"ix, 137 p.","ipdsId":"IP-127829","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":386002,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":385990,"type":{"id":15,"text":"Index Page"},"url":"https://dnr.wisconsin.gov/topic/Wells/HighCap/CSLStudy.html"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Central Sands region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.78851318359375,\n 43.58834891179792\n ],\n [\n -89.29962158203125,\n 43.57641143300888\n ],\n [\n -89.219970703125,\n 43.75919263886012\n ],\n [\n -89.54132080078125,\n 44.471031231561845\n ],\n [\n -89.7967529296875,\n 44.41808794374846\n ],\n [\n -89.85443115234375,\n 44.33367180085156\n ],\n [\n -89.98901367187499,\n 44.11125397357155\n ],\n [\n -90.01373291015625,\n 44.03232064275081\n ],\n [\n -89.96978759765625,\n 43.878097874251736\n ],\n [\n -89.8187255859375,\n 43.71156424665851\n ],\n [\n -89.78851318359375,\n 43.58834891179792\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Fienen, Michael N. 0000-0002-7756-4651","orcid":"https://orcid.org/0000-0002-7756-4651","contributorId":245632,"corporation":false,"usgs":true,"family":"Fienen","given":"Michael N.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816547,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haserodt, Megan J. 0000-0002-8304-090X mhaserodt@usgs.gov","orcid":"https://orcid.org/0000-0002-8304-090X","contributorId":174791,"corporation":false,"usgs":true,"family":"Haserodt","given":"Megan","email":"mhaserodt@usgs.gov","middleInitial":"J.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816548,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Leaf, Andrew T. 0000-0001-8784-4924 aleaf@usgs.gov","orcid":"https://orcid.org/0000-0001-8784-4924","contributorId":5156,"corporation":false,"usgs":true,"family":"Leaf","given":"Andrew","email":"aleaf@usgs.gov","middleInitial":"T.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816549,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Westenbroek, Stephen, M. 0000-0002-6284-8643","orcid":"https://orcid.org/0000-0002-6284-8643","contributorId":206429,"corporation":false,"usgs":true,"family":"Westenbroek","given":"Stephen, M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816550,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70230038,"text":"70230038 - 2021 - Assessing the uncertainties in climatic estimates based on vegetation assemblages: Examples from modern vegetation assemblages in the American Southwest","interactions":[],"lastModifiedDate":"2022-03-29T18:57:47.258287","indexId":"70230038","displayToPublicDate":"2021-05-27T08:35:57","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3219,"text":"Quaternary Science Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Assessing the uncertainties in climatic estimates based on vegetation assemblages: Examples from modern vegetation assemblages in the American Southwest","docAbstract":"Assemblages of fossil plant remains have been widely used to reconstruct past climatic conditions, usually through the application of methods that involve either finding vegetation analogues on the modern landscape (and using the modern associated climatic values as the basis for an estimate) or using the modern climatic ranges of individual taxa in an assemblage to determine the range of a given climate variable that would allow these plant taxa to live together. Although these approaches are relatively straightforward, it is difficult to assess the uncertainties associated with each approach, particularly in regard to their application to plant macrofossil assemblages. To explore the uncertainty that may arise from inaccuracy and imprecision in climate reconstructions and from ecological considerations we used variants of both approaches to estimate climate from two data sets of modern vegetation assemblages from the southwestern United States: (1) 1752 gridded “virtual plant assemblages” based on plant range maps that provide uniform spatial coverage of the presence or absence of major woody plant taxa across the study area; and (2) 43 modern packrat (Neotoma spp.) midden presence-absence assemblages that are similar to fossil midden assemblages. By comparing observed and estimated climate values, we evaluated the quality of the climate estimates, identified sources of uncertainty, and characterized the nature and magnitude of the effects of these uncertainties on the climate estimates.
Uncertainties in estimating climate from vegetation assemblages arise because any given plant taxon (or assemblage) must have the resiliency to survive a range of climatic variability, and because of the strong intercorrelations among climatic variables in the modern climate data. Additional sources of uncertainty in climate estimates from plant assemblages include: (1) the modern climate and plant distribution data that are selected as the basis for estimation; (2) the particular quantitative approach that is used to estimate climate; (3) the sufficiency of the number of taxa in the analysis for providing an unbiased representation of the vegetation community as it existed for each time period in the analysis; and, (4) the location of the assemblage on the climatic and environmental gradients in the calibration data set for each climate variable under consideration.
We conclude that vegetation assemblages can provide valid and reproducible estimates of climatic variables and that the primary trends and mapped patterns in the observed climate data can be reconstructed from such estimates. However, many factors may affect the quality of an estimate from a given plant assemblage, including aspects of data selection, data adequacy, methodologies, and the location of the assemblage site relative to gradients in the base climate data. It is particularly difficult to accurately estimate extreme values in the observed climate data, because estimated values from either end of an observed climate gradient necessarily “move toward the middle” of the gradient. In addition, the interval chosen to represent modern climate (here we used 1961 to 1990) may have a large impact on the size of the estimated difference between modern and past climate at a given site.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.quascirev.2021.106880","usgsCitation":"Thompson, R.S., Anderson, K., Pelltier, R.T., Strickland, L.E., Shafer, S., and Bartlein, P.J., 2021, Assessing the uncertainties in climatic estimates based on vegetation assemblages: Examples from modern vegetation assemblages in the American Southwest: Quaternary Science Reviews, v. 262, 106880, 27 p., https://doi.org/10.1016/j.quascirev.2021.106880.","productDescription":"106880, 27 p.","ipdsId":"IP-100351","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":436335,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CKCP22","text":"USGS data release","linkHelpText":"Data release for Assessing the Uncertainties in Climatic Estimates Based on Vegetation Assemblages: Examples from Modern Vegetation Assemblages in the American Southwest"},{"id":397596,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California, Colorado, Nevada, New Mexico, Texas, Utah","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -102.3046875,\n 29.84064389983441\n ],\n [\n -104.765625,\n 32.32427558887655\n ],\n [\n -106.435546875,\n 35.71083783530009\n ],\n [\n -106.787109375,\n 37.75334401310656\n ],\n [\n -107.666015625,\n 40.38002840251183\n ],\n [\n -111.4013671875,\n 40.51379915504413\n ],\n [\n -112.32421875,\n 42.09822241118974\n ],\n [\n -119.970703125,\n 42.06560675405716\n ],\n [\n -121.86035156249999,\n 41.83682786072714\n ],\n [\n -119.970703125,\n 38.54816542304656\n ],\n [\n -119.267578125,\n 36.24427318493909\n ],\n [\n -116.103515625,\n 32.54681317351514\n ],\n [\n -114.5654296875,\n 32.54681317351514\n ],\n [\n -111.005859375,\n 31.42866311735861\n ],\n [\n -108.28125,\n 31.316101383495624\n ],\n [\n -108.19335937499999,\n 31.914867503276223\n ],\n [\n -106.435546875,\n 31.87755764334002\n ],\n [\n -104.67773437499999,\n 30.107117887092357\n ],\n [\n -103.84277343749999,\n 29.305561325527698\n ],\n [\n -103.35937499999999,\n 29.075375179558346\n ],\n [\n -102.83203125,\n 29.19053283229458\n ],\n [\n -102.3046875,\n 29.84064389983441\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"262","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Thompson, Robert S. 0000-0001-9287-2954 rthompson@usgs.gov","orcid":"https://orcid.org/0000-0001-9287-2954","contributorId":891,"corporation":false,"usgs":true,"family":"Thompson","given":"Robert","email":"rthompson@usgs.gov","middleInitial":"S.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":838827,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Katherine H 0000-0003-2677-6109","orcid":"https://orcid.org/0000-0003-2677-6109","contributorId":289266,"corporation":false,"usgs":false,"family":"Anderson","given":"Katherine H","affiliations":[{"id":62090,"text":"Institute of Arctic and Alpine Research, University of Colorado","active":true,"usgs":false}],"preferred":false,"id":838828,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pelltier, Richard T. 0000-0001-8322-7961 rtpelltier@usgs.gov","orcid":"https://orcid.org/0000-0001-8322-7961","contributorId":4683,"corporation":false,"usgs":true,"family":"Pelltier","given":"Richard","email":"rtpelltier@usgs.gov","middleInitial":"T.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":838829,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Strickland, Laura E. 0000-0002-1958-7273 lstrickland@usgs.gov","orcid":"https://orcid.org/0000-0002-1958-7273","contributorId":4682,"corporation":false,"usgs":true,"family":"Strickland","given":"Laura","email":"lstrickland@usgs.gov","middleInitial":"E.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":838830,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shafer, Sarah 0000-0003-3739-2637 sshafer@usgs.gov","orcid":"https://orcid.org/0000-0003-3739-2637","contributorId":149866,"corporation":false,"usgs":true,"family":"Shafer","given":"Sarah","email":"sshafer@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":838831,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bartlein, Patrick J. 0000-0001-7657-5685","orcid":"https://orcid.org/0000-0001-7657-5685","contributorId":211587,"corporation":false,"usgs":false,"family":"Bartlein","given":"Patrick","email":"","middleInitial":"J.","affiliations":[{"id":33397,"text":"U of Oregon","active":true,"usgs":false}],"preferred":false,"id":838832,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70238937,"text":"70238937 - 2021 - Ten simple rules for productive lab meetings","interactions":[],"lastModifiedDate":"2022-12-19T14:19:07.541434","indexId":"70238937","displayToPublicDate":"2021-05-27T08:12:46","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5727,"text":"PLOS Computational Biology","active":true,"publicationSubtype":{"id":10}},"title":"Ten simple rules for productive lab meetings","docAbstract":"The aim of this article is to delineate 10 simple rules on how to achieve productive lab meetings. We use the term “meeting” interchangeably to represent both the single meeting event and the overarching concept of the recurring meeting. In this article we speak from our experience, as a lab group at the University of Massachusetts that meets regularly (Fig 1). Although the rules are mostly tailored toward academic or research institution settings, insights can be gained for other contexts. We believe these rules are applicable across a diverse set of labs and lab structures. For example, while many members of our current lab have remained constant for many years, the lab composition has changed as various undergraduate students, graduate students, postdoctoral fellow, visiting professors, and other faculty have joined and/or moved on. Throughout these experiences, lab rules, presented in modified form here, proved flexible and adaptable enough to be useful in helping guide productive lab meetings. Note that this article is written for principal investigator/s (PI), students, postdocs, and other lab members; it takes the whole lab group to succeed. The key to planning productive lab meetings boils down to discussing and determining as a team the answers to why, who, what, where, when, and how: Why are lab meetings important for the functioning of the lab? Who will participate? What will be the focus of lab meetings? When and where should the lab meetings occur? How should each meeting be structured and carried out so that the goals and objectives of the lab and its participants are met?
","language":"English","publisher":"Public Library of Science","doi":"10.1371/journal.pcbi.1008953","usgsCitation":"Golden, N., Devarajan, K., Balantic, C., Drake, J., Hallworth, M.T., and Morelli, T.L., 2021, Ten simple rules for productive lab meetings: PLOS Computational Biology, v. 17, no. 5, e1008953, 13 p., https://doi.org/10.1371/journal.pcbi.1008953.","productDescription":"e1008953, 13 p.","ipdsId":"IP-125473","costCenters":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":452113,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pcbi.1008953","text":"Publisher Index Page"},{"id":410702,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"17","issue":"5","noUsgsAuthors":false,"publicationDate":"2021-05-27","publicationStatus":"PW","contributors":{"editors":[{"text":"Schwartz, Russell","contributorId":300129,"corporation":false,"usgs":false,"family":"Schwartz","given":"Russell","email":"","affiliations":[{"id":12943,"text":"Carnegie Mellon University","active":true,"usgs":false}],"preferred":false,"id":859459,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Golden, Nigel","contributorId":300012,"corporation":false,"usgs":false,"family":"Golden","given":"Nigel","email":"","affiliations":[{"id":65000,"text":"University of Massachusetts, Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":859268,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Devarajan, Kadambari","contributorId":236828,"corporation":false,"usgs":false,"family":"Devarajan","given":"Kadambari","email":"","affiliations":[],"preferred":false,"id":859269,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Balantic, Cathleen","contributorId":275317,"corporation":false,"usgs":false,"family":"Balantic","given":"Cathleen","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":859270,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Drake, Joseph","contributorId":300015,"corporation":false,"usgs":false,"family":"Drake","given":"Joseph","email":"","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":859271,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hallworth, Michael T.","contributorId":213805,"corporation":false,"usgs":false,"family":"Hallworth","given":"Michael","email":"","middleInitial":"T.","affiliations":[{"id":38879,"text":"National Zoological Park, Migratory Bird Center","active":true,"usgs":false}],"preferred":false,"id":859272,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Morelli, Toni Lyn 0000-0001-5865-5294 tmorelli@usgs.gov","orcid":"https://orcid.org/0000-0001-5865-5294","contributorId":197458,"corporation":false,"usgs":true,"family":"Morelli","given":"Toni","email":"tmorelli@usgs.gov","middleInitial":"Lyn","affiliations":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":859273,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70221154,"text":"70221154 - 2021 - Transient disease dynamics across ecological scales","interactions":[],"lastModifiedDate":"2022-01-06T17:10:13.809411","indexId":"70221154","displayToPublicDate":"2021-05-27T08:12:42","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3592,"text":"Theoretical Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Transient disease dynamics across ecological scales","docAbstract":"Analyses of transient dynamics are critical to understanding infectious disease transmission and persistence. Identifying and predicting transients across scales, from within-host to community-level patterns, plays an important role in combating ongoing epidemics and mitigating the risk of future outbreaks. Moreover, greater emphases on non-asymptotic processes will enable timely evaluations of wildlife and human diseases and lead to improved surveillance efforts, preventive responses, and intervention strategies. Here, we explore the contributions of transient analyses in recent models spanning the fields of epidemiology, movement ecology, and parasitology. In addition to their roles in predicting epidemic patterns and endemic outbreaks, we explore transients in the contexts of pathogen transmission, resistance, and avoidance at various scales of the ecological hierarchy. Examples illustrate how (i) transient movement dynamics at the individual host level can modify opportunities for transmission events over time; (ii) within-host energetic processes often lead to transient dynamics in immunity, pathogen load, and transmission potential; (iii) transient connectivity between discrete populations in response to environmental factors and outbreak dynamics can affect disease spread across spatial networks; and (iv) increasing species richness in a community can provide transient protection to individuals against infection. Ultimately, we suggest that transient analyses offer deeper insights and raise new, interdisciplinary questions for disease research, consequently broadening the applications of dynamical models for outbreak preparedness and management.
A disconnect between scientific research and environmental management communities can be a detriment to both. In the case of Great Lakes coastal ecosystems, which are inherently complex and subject to uncertain effects of future climatic, environmental, and anthropogenic drivers, greater collaboration could be beneficial to their sustainability. We capture the challenges and opportunities identified by a scientist/decision-maker co-production workshop focused on the future environmental quality of Great Lakes coastal wetlands. We explain our path through the stakeholder workshop process, our challenges in translating meeting outcomes into actionable items, and lessons learned to bridge gaps between scientists and decision-makers. Additionally, we determine topics and directions identified by decision-makers that can be modeled with existing technologies and others that require further research. These topics may be incorporated into future research efforts and could serve as a shortlist of research priorities that were identified by decision-makers working with coastal wetland issues. Based on lessons learned during and after the workshop, we provide suggestions for bridging the gap between researchers and decision-makers, including sustained engagement between these groups and improved interaction through the beginning, duration, and end of research and/or management efforts.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2021.04.018","usgsCitation":"Weinstein, C.B., Bourgeau-Chavez, L., Martin, S.L., Currie, W.S., Grantham, K., Hamlin, Q.F., Hyndman, D.W., Kowalski, K., Martina, J.P., and Pearsall, D., 2021, Enhancing Great Lakes coastal ecosystems research by initiating engagement between scientists and decision-makers: Journal of Great Lakes Research, v. 47, no. 4, p. 1235-1240, https://doi.org/10.1016/j.jglr.2021.04.018.","productDescription":"6 p.","startPage":"1235","endPage":"1240","ipdsId":"IP-125315","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":387990,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Great Lakes","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.5419921875,\n 44.08758502824516\n ],\n [\n -77.3876953125,\n 44.33956524809713\n ],\n [\n -78.92578124999999,\n 44.08758502824516\n ],\n [\n -82.353515625,\n 42.58544425738491\n ],\n [\n -81.123046875,\n 43.866218006556394\n ],\n [\n -81.123046875,\n 44.37098696297173\n ],\n [\n -79.5849609375,\n 44.5278427984555\n ],\n [\n -79.62890625,\n 45.089035564831036\n ],\n [\n -81.1669921875,\n 46.28622391806706\n ],\n [\n -84.0673828125,\n 46.649436163350245\n ],\n [\n -85.517578125,\n 48.22467264956519\n ],\n [\n -87.0556640625,\n 49.18170338770663\n ],\n [\n -90.3076171875,\n 48.60385760823255\n ],\n [\n -92.94433593749999,\n 46.76996843356982\n ],\n [\n -91.7578125,\n 46.40756396630067\n ],\n [\n -88.72558593749999,\n 46.9502622421856\n ],\n [\n -88.330078125,\n 46.558860303117164\n ],\n [\n -86.8359375,\n 46.28622391806706\n ],\n [\n -85.517578125,\n 46.5286346952717\n ],\n [\n -85.0341796875,\n 46.164614496897094\n ],\n [\n -87.099609375,\n 45.920587344733654\n ],\n [\n -88.41796875,\n 44.5278427984555\n ],\n [\n -87.890625,\n 44.33956524809713\n ],\n [\n -87.978515625,\n 43.004647127794435\n ],\n [\n -87.71484375,\n 41.77131167976407\n ],\n [\n -86.572265625,\n 41.705728515237524\n ],\n [\n -85.8251953125,\n 43.068887774169625\n ],\n [\n -85.9130859375,\n 44.43377984606822\n ],\n [\n -84.72656249999999,\n 45.460130637921004\n ],\n [\n -83.8037109375,\n 44.933696389694674\n ],\n [\n -83.935546875,\n 43.77109381775651\n ],\n [\n -83.84765625,\n 43.29320031385282\n ],\n [\n -83.056640625,\n 43.89789239125797\n ],\n [\n -82.79296874999999,\n 42.94033923363181\n ],\n [\n -83.583984375,\n 41.77131167976407\n ],\n [\n -83.056640625,\n 41.07935114946899\n ],\n [\n -81.2109375,\n 41.376808565702355\n ],\n [\n -78.31054687499999,\n 42.65012181368022\n ],\n [\n -76.2451171875,\n 43.48481212891603\n ],\n [\n -75.5419921875,\n 44.08758502824516\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"47","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Weinstein, Charlotte B.","contributorId":260518,"corporation":false,"usgs":false,"family":"Weinstein","given":"Charlotte","email":"","middleInitial":"B.","affiliations":[{"id":34530,"text":"Michigan Tech Research Institute","active":true,"usgs":false}],"preferred":false,"id":821246,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bourgeau-Chavez, Laura 0000-0001-7127-279X","orcid":"https://orcid.org/0000-0001-7127-279X","contributorId":220963,"corporation":false,"usgs":false,"family":"Bourgeau-Chavez","given":"Laura","email":"","affiliations":[{"id":34530,"text":"Michigan Tech Research Institute","active":true,"usgs":false}],"preferred":false,"id":821247,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Martin, S. 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F.","contributorId":264248,"corporation":false,"usgs":false,"family":"Hamlin","given":"Q.","email":"","middleInitial":"F.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":821251,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hyndman, David W","contributorId":264249,"corporation":false,"usgs":false,"family":"Hyndman","given":"David","email":"","middleInitial":"W","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":821252,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kowalski, Kurt P. 0000-0002-8424-4701 kkowalski@usgs.gov","orcid":"https://orcid.org/0000-0002-8424-4701","contributorId":3768,"corporation":false,"usgs":true,"family":"Kowalski","given":"Kurt P.","email":"kkowalski@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":821253,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Martina, J. P.","contributorId":264250,"corporation":false,"usgs":false,"family":"Martina","given":"J.","email":"","middleInitial":"P.","affiliations":[{"id":6677,"text":"Texas State University","active":true,"usgs":false}],"preferred":false,"id":821254,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Pearsall, D.","contributorId":264252,"corporation":false,"usgs":false,"family":"Pearsall","given":"D.","email":"","affiliations":[{"id":7041,"text":"The Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":821255,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70223137,"text":"70223137 - 2021 - Impact of SST and surface waves on Hurricane Florence (2018): A coupled modeling investigation","interactions":[],"lastModifiedDate":"2021-09-21T13:11:40.385044","indexId":"70223137","displayToPublicDate":"2021-05-27T07:58:11","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3735,"text":"Weather and Forecasting","active":true,"publicationSubtype":{"id":10}},"title":"Impact of SST and surface waves on Hurricane Florence (2018): A coupled modeling investigation","docAbstract":"Hurricane Florence (2018) devastated the coastal communities of the Carolinas through heavy rainfall that resulted in massive flooding. Florence was characterized by an abrupt reduction in intensity (Saffir-Simpson Category 4 to Category 1) just prior to landfall and synoptic-scale interactions that stalled the storm over the Carolinas for several days. We conducted a series of numerical modeling experiments in coupled and uncoupled configurations to examine the impact of sea surface temperature (SST) and ocean waves on storm characteristics. In addition to experiments using a fully coupled atmosphere-ocean-wave model, we introduced the capability of the atmospheric model to modulate wind stress and surface fluxes by oceanwaves through data from an uncoupled wave model. We examined these experiments by comparing track, intensity, strength, SST, storm structure, wave height, surface roughness, heat fluxes, and precipitation in order to determine the impacts of resolving ocean conditions with varying degrees of coupling. We found differences in the storm’s intensity and strength, with the best correlation coefficient of intensity (r=0.89) and strength (r=0.95) coming from the fully-coupled simulations. Further analysis into surface roughness parameterizations added to the atmospheric model revealed differences in the spatial distribution and magnitude of the largest roughness lengths. Adding ocean andwave features to the model further modified the fluxes due to more realistic cooling beneath the stormwhich in turn modified the precipitation field. Our experiments highlight significant differences in how air-sea processes impact hurricane modeling. The storm characteristics of track, intensity, strength, and precipitation at landfall are crucial to predictability and forecasting of future landfalling hurricanes.
Forests are a significant CO2 sink. However, CO2 sequestration in forests is radiatively offset by emissions of nitrous oxide (N2O), a potent greenhouse gas, from forest soils. Reforestation, an important strategy for mitigating climate change, has focused on maximizing CO2 sequestration in plant biomass without integrating N2O emissions from soils. Although nitrogen (N)-fixing trees are often recommended for reforestation because of their rapid growth on N-poor soil, they can stimulate significant N2O emissions from soils. Here, we first used a field experiment to show that a N-fixing tree (Robinia pseudoacacia) initially mitigated climate change more than a non-fixing tree (Betula nigra). We then used our field data to parameterize a theoretical model to investigate these effects over time. Under lower N supply, N-fixers continued to mitigate climate change more than non-fixers by overcoming N limitation of plant growth. However, under higher N supply, N-fixers ultimately mitigated climate change less than non-fixers by enriching soil N and stimulating N2O emissions from soils. These results have implications for reforestation, suggesting that N-fixing trees are more effective at mitigating climate change at lower N supply, whereas non-fixing trees are more effective at mitigating climate change at higher N supply.
","language":"English","publisher":"Wiley","doi":"10.1002/ecy.3414","usgsCitation":"Kou-Giesbrecht, S., Funk, J.L., Perakis, S.S., Wolf, A.A., and Menge, D., 2021, N supply mediates the radiative balance of N2O emissions and CO2 sequestration driven by N-fixing vs. non-fixing trees: Ecology, v. 102, no. 8, e03414, 8 p., https://doi.org/10.1002/ecy.3414.","productDescription":"e03414, 8 p.","ipdsId":"IP-123004","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":452122,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecy.3414","text":"Publisher Index Page"},{"id":387284,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Black Rock Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.0584945678711,\n 41.3791271230665\n ],\n [\n -73.99017333984375,\n 41.3791271230665\n ],\n [\n -73.99017333984375,\n 41.41737138589576\n ],\n [\n -74.0584945678711,\n 41.41737138589576\n ],\n [\n -74.0584945678711,\n 41.3791271230665\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"102","issue":"8","noUsgsAuthors":false,"publicationDate":"2021-07-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Kou-Giesbrecht, Sian 0000-0002-4086-0561","orcid":"https://orcid.org/0000-0002-4086-0561","contributorId":261258,"corporation":false,"usgs":false,"family":"Kou-Giesbrecht","given":"Sian","email":"","affiliations":[{"id":52786,"text":"Columbia U","active":true,"usgs":false}],"preferred":false,"id":819602,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Funk, Jennifer L.","contributorId":260668,"corporation":false,"usgs":false,"family":"Funk","given":"Jennifer","email":"","middleInitial":"L.","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":819603,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Perakis, Steven S. 0000-0003-0703-9314 sperakis@usgs.gov","orcid":"https://orcid.org/0000-0003-0703-9314","contributorId":145528,"corporation":false,"usgs":true,"family":"Perakis","given":"Steven","email":"sperakis@usgs.gov","middleInitial":"S.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":819604,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wolf, Amelia A.","contributorId":190685,"corporation":false,"usgs":false,"family":"Wolf","given":"Amelia","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":819605,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Menge, Duncan 0000-0003-4736-9844","orcid":"https://orcid.org/0000-0003-4736-9844","contributorId":241126,"corporation":false,"usgs":false,"family":"Menge","given":"Duncan","email":"","affiliations":[{"id":7171,"text":"Columbia University","active":true,"usgs":false}],"preferred":false,"id":819606,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70221849,"text":"70221849 - 2021 - Satellite remote sensing to assess cyanobacterial bloom frequency across the United States at multiple spatial scales","interactions":[],"lastModifiedDate":"2021-07-12T17:45:19.948532","indexId":"70221849","displayToPublicDate":"2021-05-26T12:41:34","publicationYear":"2021","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":"Satellite remote sensing to assess cyanobacterial bloom frequency across the United States at multiple spatial scales","docAbstract":"Cyanobacterial blooms can have negative effects on human health and local ecosystems. Field monitoring of cyanobacterial blooms can be costly, but satellite remote sensing has shown utility for more efficient spatial and temporal monitoring across the United States. Here, satellite imagery was used to assess the annual frequency of surface cyanobacterial blooms, defined for each satellite pixel as the percentage of images for that pixel throughout the year exhibiting detectable cyanobacteria. Cyanobacterial frequency was assessed across 2,196 large lakes in 46 states across the continental United States (CONUS) using imagery from the European Space Agency’s Ocean and Land Colour Instrument for the years 2017 through 2019. In 2019, across all satellite pixels considered, annual bloom frequency had a median value of 4% and a maximum value of 100%, the latter indicating that for those satellite pixels, a cyanobacterial bloom was detected by the satellite sensor for every satellite image considered. In addition to annual pixel-scale cyanobacterial frequency, results were summarized at the lake- and state-scales by averaging annual pixel-scale results across each lake and state. For 2019, average annual lake-scale frequencies also had a maximum value of 100%, and Oregon and Ohio had the highest average annual state-scale frequencies at 65% and 52%. Pixel-scale frequency results can assist in identifying portions of a lake that are more prone to cyanobacterial blooms, while lake- and state-scale frequency results can assist in the prioritization of sampling resources and mitigation efforts. Satellite imagery is limited by the presence of snow and ice, as imagery collected in these conditions are quality flagged and discarded. Thus, annual bloom frequencies within nine climate regions were investigated to determine whether missing data biased results in climate regions more prone to snow and ice, given that their annual summaries would be weighted toward the summer months when cyanobacterial blooms tend to occur. Results were unbiased by the time period selected in most climate regions, but a large bias was observed for the Northwest Rockies and Plains climate region. Moderate biases were observed for the Ohio Valley and the Southeast climate regions. Finally, a clustering analysis was used to identify areas of high and low cyanobacterial frequency across CONUS based on average annual lake-scale cyanobacterial frequencies for 2019. Several clusters were identified that transcended state, watershed, and eco-regional boundaries. Combined with additional data, results from the clustering analysis may offer insight regarding large-scale drivers of cyanobacterial blooms.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2021.107822","usgsCitation":"Coffer, M.M., Schaeffer, B., Salls, W.B., Urquhart, E., Loftin, K.A., Stumpf, R.P., Werdell, P.J., and Darling, J., 2021, Satellite remote sensing to assess cyanobacterial bloom frequency across the United States at multiple spatial scales: Ecological Indicators, v. 128, 107822, 12 p., https://doi.org/10.1016/j.ecolind.2021.107822.","productDescription":"107822, 12 p.","ipdsId":"IP-126524","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":452125,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2021.107822","text":"Publisher Index Page"},{"id":387135,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"128","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Coffer, Megan M. 0000-0003-3188-4729","orcid":"https://orcid.org/0000-0003-3188-4729","contributorId":260857,"corporation":false,"usgs":false,"family":"Coffer","given":"Megan","email":"","middleInitial":"M.","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":818980,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schaeffer, Blake 0000-0001-9794-3977","orcid":"https://orcid.org/0000-0001-9794-3977","contributorId":245603,"corporation":false,"usgs":false,"family":"Schaeffer","given":"Blake","email":"","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":818981,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Salls, Wilson B. 0000-0001-7505-0828","orcid":"https://orcid.org/0000-0001-7505-0828","contributorId":260858,"corporation":false,"usgs":false,"family":"Salls","given":"Wilson","email":"","middleInitial":"B.","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":818982,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Urquhart, Erin 0000-0001-7141-9499","orcid":"https://orcid.org/0000-0001-7141-9499","contributorId":260859,"corporation":false,"usgs":false,"family":"Urquhart","given":"Erin","email":"","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":818983,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Loftin, Keith A. 0000-0001-5291-876X","orcid":"https://orcid.org/0000-0001-5291-876X","contributorId":221964,"corporation":false,"usgs":true,"family":"Loftin","given":"Keith","middleInitial":"A.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":818984,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stumpf, Richard P. 0000-0001-5531-6860","orcid":"https://orcid.org/0000-0001-5531-6860","contributorId":222357,"corporation":false,"usgs":false,"family":"Stumpf","given":"Richard","email":"","middleInitial":"P.","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":818985,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Werdell, P. Jeremy 0000-0002-3592-0152","orcid":"https://orcid.org/0000-0002-3592-0152","contributorId":222358,"corporation":false,"usgs":false,"family":"Werdell","given":"P.","email":"","middleInitial":"Jeremy","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":818986,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Darling, John A. 0000-0002-4776-9533","orcid":"https://orcid.org/0000-0002-4776-9533","contributorId":260860,"corporation":false,"usgs":false,"family":"Darling","given":"John A.","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":818987,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70221214,"text":"70221214 - 2021 - Seismic wave propagation and basin amplification in the Wasatch Front, Utah","interactions":[],"lastModifiedDate":"2021-11-01T15:26:45.377797","indexId":"70221214","displayToPublicDate":"2021-05-26T08:16:36","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Seismic wave propagation and basin amplification in the Wasatch Front, Utah","docAbstract":"Ground‐motion analysis of more than 3000 records from 59 earthquakes, including records from the March 2020
The Mississippi embayment encompasses about 100,000 square miles and covers parts of eight States. In 2016, the U.S. Geological Survey began updating previous work for a part of the embayment known as the Mississippi Alluvial Plain to support informed water use and agricultural policy in the region. Groundwater, water use, economic, and other related models are being combined with field surveys and observations to create a quantitative framework for evaluating regional groundwater withdrawals and their effects on long-term water availability in the Mississippi Alluvial Plain.
As part of this effort, the U.S. Geological Survey’s Soil-Water-Balance code (version 2.0) is being used to model potential groundwater recharge and irrigation water use, as necessary inputs to the long-term groundwater modeling efforts. The Soil-Water-Balance code is designed to estimate the distribution and timing of net infiltration leaving the root zone. Soil-Water-Balance makes use of gridded datasets of elevation, soils, land use (including specific crop types), and daily weather datasets to calculate other components of the root-zone water balance, including soil moisture, reference, actual evapotranspiration, snowfall, snowmelt, and canopy interception. Parameters on plant height and growing-season water needs are used to estimate crop-water demand and potential irrigation water use.
This report documents the initial construction, calibration, and application of a Soil-Water-Balance model of the Mississippi Embayment Regional Aquifer Study area for simulations running from 1915 to 2017. Further refinements of the model calibration for an expanded model area are planned.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211008","programNote":"Water Availability and Use Science Program","usgsCitation":"Westenbroek, S.M., Nielsen, M.G., and Ladd, D.E., 2021, Initial estimates of net infiltration and irrigation from a soil-water-balance model of the Mississippi Embayment Regional Aquifer Study Area: U.S. Geological Survey Open-File Report 2021-1008, 29 p., https://doi.org/10.3133/ofr20211008.","productDescription":"Report: v, 29 p.; 2 Data Releases","numberOfPages":"40","onlineOnly":"Y","ipdsId":"IP-108908","costCenters":[{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":385921,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9U484X5","text":"USGS data release","description":"USGS data release","linkHelpText":"OFR 2021–1008 MODEL OUTPUT—Soil-Water-Balance net infiltration and irrigation water use output datasets for the Mississippi Embayment Regional Aquifer System, 1915 to 2018"},{"id":385920,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98PBR8O","text":"USGS data release","description":"USGS data release","linkHelpText":"OFR 2021–1008 MODEL ARCHIVE—Soil-Water-Balance model developed to simulate net infiltration and irrigation water use for the Mississippi Embayment Regional Aquifer System, 1915 to 2018"},{"id":385919,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1008/ofr20211008.pdf","text":"Report","size":"11.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1008"},{"id":385918,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1008/coverthb.jpg"}],"country":"United States","state":"Alabama, Arkansas, Illinois, Kentucky, Louisiana, Mississippi, Missouri, Tennessee","otherGeospatial":"Mississippi Embayment Regional Aquifer Study Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.6044921875,\n 37.16031654673677\n ],\n [\n -90.4833984375,\n 36.527294814546245\n ],\n [\n -91.2744140625,\n 35.71083783530009\n ],\n [\n -91.7138671875,\n 35.31736632923788\n ],\n [\n -92.4169921875,\n 35.02999636902566\n ],\n [\n -93.3837890625,\n 34.59704151614417\n ],\n [\n -94.0869140625,\n 33.578014746143985\n ],\n [\n -94.1748046875,\n 32.69486597787505\n ],\n [\n -93.6474609375,\n 31.690781806136822\n ],\n [\n -92.94433593749999,\n 31.50362930577303\n ],\n [\n -91.93359375,\n 31.42866311735861\n ],\n [\n -91.14257812499999,\n 31.541089879585808\n ],\n [\n -89.7802734375,\n 31.316101383495624\n ],\n [\n -88.5498046875,\n 30.86451022625836\n ],\n [\n -87.3193359375,\n 31.203404950917395\n ],\n [\n -87.5390625,\n 31.80289258670676\n ],\n [\n -88.5498046875,\n 32.69486597787505\n ],\n [\n -88.505859375,\n 33.8339199536547\n ],\n [\n -88.5498046875,\n 34.59704151614417\n ],\n [\n -88.3740234375,\n 35.60371874069731\n ],\n [\n -88.5498046875,\n 36.94989178681327\n ],\n [\n -89.6044921875,\n 37.16031654673677\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
8505 Research Way
Middleton, WI 53562
Groundwater quality in the northern Sierra Nevada foothills region of California was investigated as part of California State Water Resources Control Board (SWRCB) Groundwater Ambient Monitoring Assessment Priority Basin Project (GAMA-PBP). The region was divided into two study units: the Yuba-Bear watersheds (YBW) study unit and the American-Cosumnes-Mokelumne watersheds (ACMW) study unit. The GAMA-PBP made a spatially unbiased assessment of aquifer systems used for domestic drinking-water supply in the study region, which are predominantly composed of fractured, hard-rock aquifers of varying lithology. These assessments characterized the quality of raw groundwater to evaluate ambient conditions in the domestic-supply aquifer and not the quality of treated drinking water.
The study included three components: (1) a status assessment, which characterized the quality of groundwater resources used for domestic drinking-water supply in the YBW and ACMW study units; (2) an understanding assessment, which evaluated natural and anthropogenic explanatory factors that could potentially affect groundwater quality in the study region; and (3) a comparative assessment between the groundwater resources used for domestic and public drinking-water supply in the study region.
The status assessment was based on data collected by the GAMA-PBP from 74 sites in the YBW study unit during 2015–16 and 67 sites in the ACMW study unit from 2016 to 2017. To contextualize water-quality results, concentrations of water-quality constituents in ambient groundwater were compared to regulatory and non-regulatory benchmarks typically used by the State of California and Federal agencies as health-based or aesthetic standards for public drinking water. The status assessment used a grid-based method to estimate proportions of groundwater resources with concentrations approaching or exceeding benchmark thresholds. This method provides spatially unbiased results and allows inter-comparability with similar groundwater-quality assessments.
Inorganic constituents with health-based benchmarks were present at high relative concentration (RC), meaning they exceeded the benchmark threshold, in 5.4 and 10 percent of domestic-supply aquifer systems in the YBW and ACMW study units, respectively. Inorganic constituents with aesthetic-based benchmarks were detected at high-RCs in 20 and 28 percent of the YBW and ACMW study units, respectively. The inorganic constituents present at high RC were arsenic, barium, boron, molybdenum, strontium, nitrate, adjusted gross-alpha particle activity, chloride, total dissolved solids, specific conductance, iron, manganese, and hardness. Groundwater samples were tested for presence or absence of three microbial indicators (total coliform, Escherichia coli, and Enterococci). At least one microbial indicator was present in 26 and 28 percent of the YBW and ACMW study units, respectively. At least one organic constituent was detected in 30 and 42 percent of the YBW and ACMW study units, respectively. Organic constituents were not present at high RC, but tetrachloroethene (PCE), trichloroethene (TCE), and toluene were detected in the YBW study unit at moderate RC (between the benchmark concentration and one-tenth of the benchmark concentration). Methyl tert-butyl ether (MTBE) and chloroform were present at low RC (less than one-tenth of the benchmark concentration) in the YBW and ACMW study units with detection frequencies greater than 10 percent. Perchlorate, a constituent of special interest, was detected in 31 and 41 percent of the YBW and ACMW study units, respectively, at either low or moderate RCs.
Relations among select water-quality constituents and potential explanatory factors were evaluated using statistical and graphical approaches. Nitrate, microbial indicators, and perchlorate were all correlated to elevation-dependent variables relating to climate, land use, and recharge condition. Isotopic and dissolved noble-gas tracers indicated these water-quality constituents are associated with recharge conditions associated with irrigation during the summer dry-season, which is common in areas of rural-residential or agricultural land uses. Higher concentrations of iron and manganese were primarily associated with anoxic groundwater in aquifers of metasedimentary lithology. Increased hardness was primarily associated with anoxic groundwater in aquifers of mafic-ultramafic or metavolcanics lithologies at lower elevations in the study region in the Melones fault zone. Chloroform and MTBE were associated with shallow groundwater (wells depths less than 130 m) under oxic and anoxic redox conditions, respectively.
The comparative assessment evaluated differences between the aquifer systems used for domestic- and public-supply in study region based on (1) well-construction characteristics, and (2) water quality. Analysis of over 60,000 well-completion reports in the study region showed that although domestic-supply wells span the deepest depth zones in regional aquifers, median depths for public-supply wells were significantly greater than those of domestic-supply wells in both study units. Water-quality data from more than 300 public-supply wells in the study region were assessed using a spatially weighted method for calculation aquifer-scale proportions and compared with the domestic-supply assessment results. Detections of inorganic constituents at high RC and detection frequencies for organic constituents were generally similar between the domestic- and public-supply aquifer systems in both study units, with a few notable exceptions in the ACMW study unit: nitrate was greater for the public- compared to domestic-supply aquifer system and both manganese, hardness, and MTBE were greater in the domestic- compared to public-supply aquifer system. These differences are likely related to contrasting land uses, aquifer lithologies, landscape positions, and depths characterizing domestic- and public-supply wells in the ACMW study unit.
Overall, fewer samples from domestic-supply wells in the northern Sierra Nevada foothills exceeded health-based benchmarks compared to aesthetic-based benchmarks for groundwater quality. Exceedences of health-based benchmarks were primarily caused by nitrate and coliform bacteria, which were associated with recharge from diverted surface water used primarily for irrigation. Exceedences of aesthetic-based benchmarks were primarily caused by iron, managanese, and hardness, which were associated with geologic factors. Regional irrigation practices and aquifer lithology can affect groundwater quality in fractured-rock aquifers in the northern Sierra Nevada foothills used for domestic drinking-water supply.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215019","collaboration":"Prepared in cooperation with the California State Water Resources Control BoardDirector,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
The U.S. Geological Survey, in cooperation with the Muskingum Watershed Conservancy District and the city of Rittman, Ohio, did a study to provide data to update and expand parts of two Federal Emergency Management Agency Flood Insurance Studies. The study consisted of hydrologic and hydraulic analyses for selected reaches of four streams (Chippewa Creek, Little Chippewa Creek, Styx River, and the unnamed tributary to Styx River) near the city of Rittman in Wayne and Medina Counties, Ohio. The study covered 36.2 miles of stream reaches.
Instantaneous peak streamflows for floods with 10-, 4-, 2-, 1-, and 0.2-percent and 1-percent plus annual exceedance probabilities were estimated using historical streamflow data from three U.S. Geological Survey streamgages and regional flood-frequency regression equations. The flood-frequency estimates were then used in a Hydrologic Engineering Center River Analysis System step-backwater model to determine water-surface profiles; flood-inundation boundaries for the 10-, 4-, 2-, 1-, and 0.2-percent and 1-percent plus annual exceedance probabilities; and a regulatory floodway for the study reaches. Model inputs included cross sections derived from a digital elevation model supplemented with field surveys of open-channel cross sections and hydraulic structures, field estimates of Manning’s roughness values, and flood estimates determined from regional regression equations and historical streamflow data. Flood-inundation boundaries were mapped for each stream reach for the 1- and 0.2-percent annual exceedance probability floods and a regulatory floodway. All data used in the creation of the flood-inundation boundaries are available through a U.S. Geological Survey data release (Ostheimer, 2021) and will be submitted to the Federal Emergency Management Agency for inclusion in updated Flood Insurance Studies for Wayne and Medina Counties.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215040","collaboration":"Prepared in cooperation with the city of Rittman and the Muskingum Watershed Conservancy District","usgsCitation":"Ostheimer, C.J., 2021, Hydrologic and hydraulic analyses of selected streams near the city of Rittman in Wayne and Medina Counties, Ohio: U.S. Geological Survey Scientific Investigations Report 2021–5040, 30 p., https://doi.org/10.3133/sir20215040.","productDescription":"Report: iv, 30 p.; Data Release","numberOfPages":"38","onlineOnly":"Y","ipdsId":"IP-117425","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":385929,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9W6ROMC","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Geospatial data sets and hydraulic models for selected streams near Rittman in Wayne and Medina Counties, Ohio"},{"id":385927,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5040/coverthb.jpg"},{"id":385956,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5040/images"},{"id":385928,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5040/sir20215040.pdf","text":"Report","size":"7.43 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5040"}],"country":"United States","state":"Ohio","county":"Wayne County, Medina County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-81.6845,41.2772],[-81.6885,40.9887],[-81.6477,40.9884],[-81.648,40.9145],[-81.6483,40.7371],[-81.6491,40.6681],[-82.126,40.6682],[-82.1266,40.778],[-82.1292,40.9921],[-82.1736,40.9922],[-82.1722,41.0435],[-82.1714,41.0639],[-82.1699,41.1251],[-82.1699,41.1369],[-82.0741,41.1362],[-82.0725,41.2001],[-81.9736,41.1998],[-81.9724,41.2747],[-81.8777,41.2747],[-81.7848,41.2765],[-81.6845,41.2772]]]},\"properties\":{\"name\":\"Medina\",\"state\":\"OH\"}}]}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
6460 Busch Blvd., Suite 100
Columbus, OH 43229
Prior to the 2018 lower East Rift Zone (ERZ) eruption and summit collapse of Kīlauea Volcano, Hawai‘i, continuous gravimeters operated on the vent rims of ongoing eruptions at both the summit and Pu‘u ‘Ō‘ō. These instruments captured the onset of the 2018 lower ERZ eruption and the effects of lava withdrawal from both locales, providing constraints on the timing and style of activity and the physical properties of the lava lakes at both locations. At the summit, combining gravity, lava level, and a three-dimensional model of the vent indicates that the upper ∼200 m of the lava lake had a density of about 1700 kg m−3, slightly greater than estimates from 2011–2015 and possibly indicating a gradual densification over time. At Pu‘u ‘Ō‘ō, gravity and vent geometry were used to model both the density and the rate of crater collapse, which was unknown owing to a lack of visual observations. Results suggest the withdrawal of at least 11×106 m3 of lava over the course of two hours, and a material density of 1800–1900 kg m−3. In addition, gravity data at Pu‘u ‘Ō‘ō captured a transient decrease and increase about an hour prior to crater collapse and that was probably related to a small, short-lived fissure eruption on the west flank of the cone and possibly to dike intrusion beneath Pu‘u ‘Ō‘ō. The fissure was the first event in the subsequent cascade that ultimately led to the extrusion of over 1 km3 of lava from lower ERZ vents, collapse of the summit caldera floor by more than 500 m, and the destruction of over 700 homes and other structures. These results emphasize the importance of continuous gravity in operational monitoring of active volcanoes.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.epsl.2021.117003","usgsCitation":"Poland, M., Carbone, D., and Patrick, M.R., 2021, Onset and evolution of Kilauea’s 2018 flank eruption and summit collapse from continuous gravity: Earth and Planetary Science Letters, v. 567, 117003, 12 p., https://doi.org/10.1016/j.epsl.2021.117003.","productDescription":"117003, 12 p.","ipdsId":"IP-123201","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":452142,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.epsl.2021.117003","text":"Publisher Index Page"},{"id":436343,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99QB29I","text":"USGS data release","linkHelpText":"Crater geometry data for Puʻuʻōʻō, on Kīlauea Volcano&rsquo;s East Rift Zone, in May 2018"},{"id":436342,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PP5LX1","text":"USGS data release","linkHelpText":"Continuous gravity data from K?lauea Volcano, Hawai?i"},{"id":397686,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kilauea Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.40985107421875,\n 19.158141038187704\n ],\n [\n -154.78912353515625,\n 19.158141038187704\n ],\n [\n -154.78912353515625,\n 19.557202031700292\n ],\n [\n -155.40985107421875,\n 19.557202031700292\n ],\n [\n -155.40985107421875,\n 19.158141038187704\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"567","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Poland, Michael 0000-0001-5240-6123","orcid":"https://orcid.org/0000-0001-5240-6123","contributorId":49920,"corporation":false,"usgs":true,"family":"Poland","given":"Michael","affiliations":[{"id":336,"text":"Hawaiian Volcano Observatory","active":false,"usgs":true}],"preferred":true,"id":838947,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carbone, Daniele","contributorId":124561,"corporation":false,"usgs":false,"family":"Carbone","given":"Daniele","email":"","affiliations":[{"id":5113,"text":"INGV","active":true,"usgs":false}],"preferred":false,"id":838948,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Patrick, Matthew R. 0000-0002-8042-6639 mpatrick@usgs.gov","orcid":"https://orcid.org/0000-0002-8042-6639","contributorId":2070,"corporation":false,"usgs":true,"family":"Patrick","given":"Matthew","email":"mpatrick@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":838949,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70221158,"text":"70221158 - 2021 - Synthesizing and analyzing long-term monitoring data: A greater sage-grouse case study","interactions":[],"lastModifiedDate":"2021-06-07T11:51:59.056332","indexId":"70221158","displayToPublicDate":"2021-05-25T07:40:54","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1457,"text":"Ecological Informatics","active":true,"publicationSubtype":{"id":10}},"title":"Synthesizing and analyzing long-term monitoring data: A greater sage-grouse case study","docAbstract":"Long-term monitoring of natural resources is imperative for increasing the understanding of ecosystem processes, services, and how to manage those ecosystems to maintain or improve function. Challenges with using these data may occur because methods of monitoring changed over time, multiple organizations collect and manage data differently, and monetary resources fluctuate, affecting many aspects of data. Because many species respond to changes in habitat conditions and predator-prey relationships across different spatial scales that span management boundaries, greater efforts for collaborating are essential. We demonstrate the challenges and methods for standardizing greater sage-grouse (Centrocercus urophasianus) long-term monitoring data across the species range in the western United States to inform population modeling needs identified by the Western Association of Fish and Wildlife Agencies. We used automated and repeatable methods of standardizing data via custom open-source software (grsg_lekdb) to improve the scientific integrity of future sage-grouse population assessments within and among states. Data standardization included reconciling uses of different terminology and expunging unusable data, resulting in the removal of 26% of data records due to database insertion errors and modifications to >1 million values to correct formatting and typing errors. Our approaches maximized the inclusion of usable data and identified data that could inform detection probabilities, population trends, and monitoring guidelines. Using sage-grouse databases as an example, we identified the importance of data management and how quality assurance and quality control measures can improve the usefulness of these data for future research needs. Our methods of using informatics and concluding recommendations can support similar endeavors of flora and fauna monitoring programs, whether those efforts are to use existing data or support new monitoring programs.
=
The western pond turtle (WPT) was formerly considered a single species (Actinemys or Emys marmorata) that ranged from southern British Columbia, Canada to Baja California, México. More recently it was divided into a northern and a southern species. WPTs are found primarily in streams that drain into the Pacific Ocean, although scattered populations exist in endorheic drainages of the Great Basin and Mojave deserts. Populations in the Mojave Desert were long thought to be restricted to the Mojave River, but recently another population was documented in Piute Ponds, a terminal wetland complex associated with Amargosa Creek on Edwards Air Force Base. WPT fossils in the Mojave Desert are known from the Miocene to the Pleistocene. Recently, Pleistocene fossils have been found as far into the desert as Salt Springs, just south of Death Valley. The oldest fossil records suggest that WPTs were present in wetlands and drainages of the geological feature known as the Mojave block prior to the uplift of the Sierra Nevada Range about 8 Ma and prior to the ~ 3 Ma uplift of the Transverse Ranges. Archaeological records document use of turtles by Native Americans for food and cultural purposes 1,000 or more years ago at the Cronese Lakes on the lower Mojave River and Oro Grande on the upper river. The first modern publication documenting their presence in the Mojave River was 1861. Museum specimens were collected as early as 1937. These fossil and early literature records support the indigenous status of WPTs to the Mojave River. However, mtDNA-based genetic evidence shows that Mojave River turtles share an identical haplotype with turtles on the California coast. Limited nuclear data show some minor differences. Overdraft of water from the Mojave River for municipal and agricultural uses, urban development, and saltcedar expansion are threats to the continued survival of WPTs in the Mojave River.