Super gages are an important tool providing real-time, continuous water-quality data at streamgages or groundwater wells. They are designed to address specific water-resource threats such as water-related human health issues including harmful algal blooms, floods, droughts, and hazardous substance spills. In addition, super gages improve our understanding of the effects land-use practices have on critical water resources. Super gage data allow the development of surrogates, a continuous in-stream sensor measurement used to estimate something of greater interest to environmental managers, to be modeled and reported in near real-time concentrations and loads. This fact sheet presents some of the ways water-quality data from a USGS super gage network benefits all of us.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203019","collaboration":"Prepared in cooperation with the Kentucky Governor's Office of Agricultural Policy","usgsCitation":"Crain, A.S., 2020, The importance of U.S. Geological Survey water-quality super gages: U.S. Geological Survey Fact Sheet 2020–3019, 2 p., https://doi.org/10.3133/fs20203019.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-113930","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":374216,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3019/coverthb.jpg"},{"id":374217,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3019/fs20203019.pdf","text":"Report","size":"2.25 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2020–3019"}],"contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
9818 Bluegrass Parkway
Louisville, KY 40299
Riverscape genetics, which applies concepts in landscape genetics to riverine ecosystems, lack appropriate quantitative methods that address the spatial autocorrelation structure of linear stream networks and account for bidirectional geneflow. To address these challenges, we present a general framework for the design and analysis of riverscape genetic studies. Our framework starts with the estimation of pairwise genetic distance at sample sites and the development of a spatially structured ecological network (SSEN) on which riverscape covariates are measured. We then introduce the novel bidirectional geneflow in riverscapes (BGR) model that uses principles of isolation-by-resistance to quantify the effects of environmental covariates on genetic connectivity, with spatial covariance defined using simultaneous autoregressive models on the SSEN and the generalized Wishart distribution to model pairwise distance matrices arising through a random walk model of geneflow. We highlight the utility of this framework in an analysis of riverscape genetics for brook trout (Salvelinus fontinalis) in north central Pennsylvania, USA. Using the fixation index (FST) as the measure of genetic distance, we estimated the effects of 12 riverscape covariates on geneflow by evaluating the relative support of eight competing BGR models. We then compared the performance of the top-ranked BGR model to results obtained from comparable analyses using multiple regression on distance matrices (MRM) and the program STRUCTURE. We found that the BGR model had more power to detect covariate effects, particularly for variables that were only partial barriers to geneflow and/or uncommon in the riverscape, making it more informative for assessing patterns of population connectivity and identifying threats to species conservation. This case study highlights the utility of our modeling framework over other quantitative methods in riverscape genetics, particularly the ability to rigorously test hypotheses about factors that influence geneflow and probabilistically estimate the effect of riverscape covariates, including stream flow direction. This framework is flexible across taxa and riverine networks, is easily executable, and provides intuitive results that can be used to investigate the likely outcomes of current and future management scenarios.
I present a prescription for computing free-air coseismic and post-seismic gravity changes induced by seismic sources in a viscoelastic earth model. I assume a spherical earth geometry and a 2.5-D calculation, that is, 3-D motions that satisfy the equations of quasi-static equilibrium on a 2-D viscoelastic structure. The prescription permits application to regional gravity computations where a 2-D structure adequately represents the structural heterogeneity. I use a hybrid approach where deformation is computed on a discretized domain and the resulting density perturbations are expanded with spherical harmonics to produce the free-air gravity field. Starting with a solution to the equations of quasi-static displacements in the Laplace transform domain for a given dislocation source, I solve Poisson’s equation using Lagrangian interpolation on spectral element nodes to compute the required deformation quantities that contribute to free-air gravity. A numerical inverse Laplace transform then yields time domain results. This methodology is tested with analytic solutions on a spherically stratified viscoelastic structure, then applied to evaluate the effect of a descending slab of relatively high viscosity on post-seismic gravity in a megathrust faulting setting.
","language":"English","publisher":"Royal Astronomical Society","doi":"10.1093/gji/ggaa151","usgsCitation":"Pollitz, F., 2020, Coseismic and post-seismic gravity disturbance induced by seismic sources using a 2.5-D spectral element method: Geophysical Journal International, v. 122, no. 2, p. 827-844, https://doi.org/10.1093/gji/ggaa151.","productDescription":"18 p.","startPage":"827","endPage":"844","ipdsId":"IP-112024","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":456958,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/gji/ggaa151","text":"Publisher Index Page"},{"id":420296,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"122","issue":"2","noUsgsAuthors":false,"publicationDate":"2020-04-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Pollitz, Frederick 0000-0002-4060-2706 fpollitz@usgs.gov","orcid":"https://orcid.org/0000-0002-4060-2706","contributorId":139578,"corporation":false,"usgs":true,"family":"Pollitz","given":"Frederick","email":"fpollitz@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":881376,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70211280,"text":"70211280 - 2020 - Modelling grass carp egg transport using a 3-D hydrodynamic river model: The role of egg retention in dead zones on spawning success","interactions":[],"lastModifiedDate":"2020-08-04T14:30:08.001153","indexId":"70211280","displayToPublicDate":"2020-04-24T10:36:35","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Modelling grass carp egg transport using a 3-D hydrodynamic river model: The role of egg retention in dead zones on spawning success","docAbstract":"Invasive grass carp (Ctenopharyngodon idella) are known to spawn in the Sandusky River, Ohio, USA, within the Great Lakes Basin, and are threatening to expand throughout the Great Lakes. Successful spawning is thought to require that eggs remain in suspension until hatching, which depends on river hydrodynamics and temperature-dependent egg development. Previous modelling efforts used one-dimensional hydrodynamic models that simplify egg movement by not simulating low-velocity zones within the river. To examine the effect of low-velocity zones on egg transit times and hatching rates, we developed a novel coupling of a biophysical Lagrangian particle tracker and three-dimensional hydrodynamic model on the Sandusky River during a high-flow event. The model successfully predicted egg-capture data for a range of developmental stages and revealed a mechanism that resuspends eggs trapped in low-velocity zones. The resuspension mechanism increases the residence time of grass carp eggs in spawning tributaries and can lead to successful hatching occurring in shorter distances than previously estimated. Grass carp potentially spawning in shorter tributary lengths has widespread implications for efforts preventing establishment in the Great Lakes Basin.","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2019-0344","usgsCitation":"Heer, T., Wells, M.G., Jackson, P.R., and Mandrak, N.E., 2020, Modelling grass carp egg transport using a 3-D hydrodynamic river model: The role of egg retention in dead zones on spawning success: Canadian Journal of Fisheries and Aquatic Sciences, v. 77, no. 8, p. 1379-1392, https://doi.org/10.1139/cjfas-2019-0344.","productDescription":"14 p.","startPage":"1379","endPage":"1392","ipdsId":"IP-109963","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":501026,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/1807/101564","text":"External Repository"},{"id":437014,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7M9080M","text":"USGS data release","linkHelpText":"Velocity, Discharge, and Dye Concentrations During a Dye Tracer Study on the Lower Sandusky River, Ohio, July 11-13, 2017"},{"id":376640,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Ohio","otherGeospatial":"Sandusky River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.199462890625,\n 40.6723059714534\n ],\n [\n -82.408447265625,\n 40.6723059714534\n ],\n [\n -82.408447265625,\n 41.549700145132725\n ],\n [\n -83.199462890625,\n 41.549700145132725\n ],\n [\n -83.199462890625,\n 40.6723059714534\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"77","issue":"8","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Heer, Tej","contributorId":229535,"corporation":false,"usgs":false,"family":"Heer","given":"Tej","email":"","affiliations":[{"id":7044,"text":"University of Toronto","active":true,"usgs":false}],"preferred":false,"id":793486,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wells, Mathew G.","contributorId":229536,"corporation":false,"usgs":false,"family":"Wells","given":"Mathew","email":"","middleInitial":"G.","affiliations":[{"id":7044,"text":"University of Toronto","active":true,"usgs":false}],"preferred":false,"id":793487,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jackson, P. Ryan 0000-0002-3154-6108 pjackson@usgs.gov","orcid":"https://orcid.org/0000-0002-3154-6108","contributorId":194529,"corporation":false,"usgs":true,"family":"Jackson","given":"P.","email":"pjackson@usgs.gov","middleInitial":"Ryan","affiliations":[{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true},{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":793488,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mandrak, Nicholas E.","contributorId":177869,"corporation":false,"usgs":false,"family":"Mandrak","given":"Nicholas","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":793489,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70210685,"text":"70210685 - 2020 - A two-stage step-wise framework for fast optimization of well placement in coalbed methane reservoirs","interactions":[],"lastModifiedDate":"2020-06-17T13:25:49.261404","indexId":"70210685","displayToPublicDate":"2020-04-24T08:22:56","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2033,"text":"International Journal of Coal Geology","active":true,"publicationSubtype":{"id":10}},"title":"A two-stage step-wise framework for fast optimization of well placement in coalbed methane reservoirs","docAbstract":"Coalbed methane (CBM) has emerged as a clean energy resource in the global energy mix, especially in countries such as Australia, China, India and the USA. The economical and successful development of CBM requires a thorough evaluation and optimization of well placement prior to field-scale exploitation. This paper presents a two-stage, step-wise optimization framework to obtain the optimal placement of wells for large-scale development of CBM reservoirs. In the first stage, an optimal uniform well pattern is obtained by optimizing well pattern description parameters with the particle swarm optimization (PSO) algorithm. Subsequently, the location and status (active/inactive) of each well are perturbed and optimized within the patterns through the integration of the generalized pattern search (GPS) algorithm and a quality map (QM) representing the production potential. This framework was tested in a synthetic anthracite CBM reservoir in the Qinshui basin (with high gas content and low permeability) and a real field high volatile bituminous reservoir in the Illinois basin (with low gas content and high permeability). The results show that: (i) significant variations in the net present value (NPV) exist with respect to different uniform well patterns (even for cases where the total number of wells are identical), the optima of which can be efficiently determined by the PSO within 100 numerical simulation runs; (ii) the optimization of well perturbations by the GPS results in a more noticeable improvement in NPVs for the synthetic (12.3%) than for the real field model (4.6%); (iii) for the low permeable synthetic model with narrow optimal well spacings (320 m × 200 m), the contribution of the optimization of well perturbation to the NPV increment is heavily dependent on the uniform well placement solution; (iv) for the high permeable real field model with large optimal well spacings (1300 m × 1300 m), the initial uniform well placement has a very minor effect on the subsequent well perturbation solutions in terms of NPV; (v) the proposed framework significantly outperforms the conventional well-by-well concatenation procedure in terms of computational efficiency, robustness and optimal criteria set for production potential.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.coal.2020.103479","usgsCitation":"Zhang, J., Feng, Q., Zhang, X., Bai, J., Karacan, C.O., and Elsworth, D., 2020, A two-stage step-wise framework for fast optimization of well placement in coalbed methane reservoirs: International Journal of Coal Geology, v. 225, 103479, 16 p., https://doi.org/10.1016/j.coal.2020.103479.","productDescription":"103479, 16 p.","ipdsId":"IP-111995","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":375662,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"225","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Zhang, Jiyuan","contributorId":225384,"corporation":false,"usgs":false,"family":"Zhang","given":"Jiyuan","email":"","affiliations":[],"preferred":false,"id":790966,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Feng, Qihong","contributorId":225385,"corporation":false,"usgs":false,"family":"Feng","given":"Qihong","email":"","affiliations":[],"preferred":false,"id":790967,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhang, Xianmin","contributorId":225386,"corporation":false,"usgs":false,"family":"Zhang","given":"Xianmin","email":"","affiliations":[],"preferred":false,"id":790968,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bai, Jia","contributorId":225387,"corporation":false,"usgs":false,"family":"Bai","given":"Jia","email":"","affiliations":[],"preferred":false,"id":790969,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Karacan, C. Ozgen 0000-0002-0947-8241","orcid":"https://orcid.org/0000-0002-0947-8241","contributorId":201991,"corporation":false,"usgs":true,"family":"Karacan","given":"C.","email":"","middleInitial":"Ozgen","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":790965,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Elsworth, Derek","contributorId":225388,"corporation":false,"usgs":false,"family":"Elsworth","given":"Derek","affiliations":[],"preferred":false,"id":790970,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70209691,"text":"70209691 - 2020 - The role of seismic and slow slip events in triggering the 2018 M7.1 Anchorage earthquake in the Southcentral Alaska subduction zone","interactions":[],"lastModifiedDate":"2020-06-03T13:40:17.693808","indexId":"70209691","displayToPublicDate":"2020-04-23T17:01:55","publicationYear":"2020","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":"The role of seismic and slow slip events in triggering the 2018 M7.1 Anchorage earthquake in the Southcentral Alaska subduction zone","docAbstract":"The M 7.1 2018 Anchorage earthquake occurred in the bending part of the subducting North Pacific plate near the geometrical barrier formed by the underthrusting Yakutat terrane. We calculate the triggering potential related with stress redistribution from deformation sources including the M 9.2 1964 earthquake coseismic slip, postseismic deformation, slip from regional M > 5 earthquakes, and the cumulative slip of previously detected slow slip events over the past 55 years. We investigate the deeper shallow depth (20–60 km) seismicity response to these perturbations using an epidemic type aftershock sequence model to describe earthquake‐to‐earthquake interactions. The statistical forecast captures the triggered seismicity during the 1983 M 6+ aftershocks in Columbia Bay but performs poorly during the slow slip event period between 1992.0 and 2004.8 that presents a statistically significant rate change (β , Z > 2; M < 4.0). We find that stress effects from the 1964 postseismic source and the 12‐year‐long slow slip event (~M 7.8) contribute to the 2018 Anchorage earthquake occurrence and that slow slip events modulate the deeper shallow depth seismicity patterns in the region.
The North American Breeding Bird Survey (BBS) provides data that can be used in complex, multiscale analyses of population change, while controlling for scale‐specific nuisance factors. Many alternative models can be fit to the data, but most model selection procedures are not appropriate for hierarchical models. Leave‐one‐out cross‐validation (LOOCV), in which relative model fit is assessed by omitting an observation and assessing the prediction of a model fit using the remainder of the data, provides a reasonable approach for assessing models, but is time consuming and not feasible to apply for all observations in large data sets. We report the first large‐scale formal model selection for BBS data, applying LOOCV to stratified random samples of observations from BBS data. Our results are for 548 species of North American birds, comparing the fit of four alternative models that differ in year effect structures and in descriptions of extra‐Poisson overdispersion. We use a hierarchical model among species to evaluate posterior probabilities that models are best for individual species. Models in which differences in year effects are conditionally independent (D models) were generally favored over models in which year effects are modeled by a slope parameter and a random year effect (S models), and models in which extra‐Poisson overdispersion effects are independent and t‐distributed (H models) tended to be favored over models where overdispersion was independent and normally distributed. Our conclusions lead us to recommend a change from the conventional S model to D and H models for the vast majority of species (544/548). Comparison of estimated population trends based on the favored model relative to the S model currently used for BBS summaries indicates no consistent differences in estimated trends. Of the 18 species that showed large differences in estimated trends between models, estimated trends from the default S model were more extreme, reflecting the influence of the slope parameter in that model for species that are undergoing large population changes. WAIC, a computationally simpler alternative to LOOCV, does not appear to be a reliable alternative to LOOCV.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2137","usgsCitation":"Link, W.A., Sauer, J.R., and Niven, D.K., 2020, Model selection for the North American Breeding Bird Survey: Ecological Applications, v. 30, no. 6, e2037, 10 p., https://doi.org/10.1002/eap.2137.","productDescription":"e2037, 10 p.","ipdsId":"IP-112644","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":377210,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"North America","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.2109375,\n 7.013667927566642\n ],\n [\n -71.015625,\n 20.3034175184893\n ],\n [\n -77.34374999999999,\n 28.92163128242129\n ],\n [\n -68.5546875,\n 40.713955826286046\n ],\n [\n -50.625,\n 49.15296965617042\n ],\n [\n -62.22656249999999,\n 68.65655498475735\n ],\n [\n -84.375,\n 76.67978490310692\n ],\n [\n -123.04687499999999,\n 77.61770905279676\n ],\n [\n -131.1328125,\n 71.52490903732816\n ],\n [\n -159.2578125,\n 71.85622888185527\n ],\n [\n -166.9921875,\n 69.03714171275197\n ],\n [\n -166.9921875,\n 62.75472592723178\n ],\n [\n -162.7734375,\n 58.07787626787517\n ],\n [\n -162.421875,\n 54.97761367069628\n ],\n [\n -148.0078125,\n 56.36525013685606\n ],\n [\n -141.328125,\n 57.326521225217064\n ],\n [\n -134.296875,\n 54.36775852406841\n ],\n [\n -127.265625,\n 47.040182144806664\n ],\n [\n -126.21093749999999,\n 37.16031654673677\n ],\n [\n -116.01562499999999,\n 26.43122806450644\n ],\n [\n -104.0625,\n 14.944784875088372\n ],\n [\n -81.2109375,\n 7.013667927566642\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"30","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-06-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Link, William A. 0000-0002-9913-0256 wlink@usgs.gov","orcid":"https://orcid.org/0000-0002-9913-0256","contributorId":146920,"corporation":false,"usgs":true,"family":"Link","given":"William","email":"wlink@usgs.gov","middleInitial":"A.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":795277,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sauer, John R. 0000-0002-4557-3019 jrsauer@usgs.gov","orcid":"https://orcid.org/0000-0002-4557-3019","contributorId":146917,"corporation":false,"usgs":true,"family":"Sauer","given":"John","email":"jrsauer@usgs.gov","middleInitial":"R.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":795278,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Niven, Daniel K 0000-0002-3253-7211 dniven@usgs.gov","orcid":"https://orcid.org/0000-0002-3253-7211","contributorId":237775,"corporation":false,"usgs":true,"family":"Niven","given":"Daniel","email":"dniven@usgs.gov","middleInitial":"K","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":795279,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70228354,"text":"70228354 - 2020 - Comparing environmental flow implementation options with structured decision making: Case study from the Willamette River, Oregon","interactions":[],"lastModifiedDate":"2022-02-09T20:59:03.745231","indexId":"70228354","displayToPublicDate":"2020-04-23T14:46:32","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"Comparing environmental flow implementation options with structured decision making: Case study from the Willamette River, Oregon","docAbstract":"Many frameworks have been used to identify environmental flows for sustaining river ecosystems or specific taxa in the face of widespread flow alteration for human use. However, these methods mostly focus on identifying suitable flows and largely ignore the important links between management actions, resulting flows, flow variability, and ecosystem or social responses. Structured decision making (SDM) could assist the comparison and implementation of environmental flows by providing a framework to compare effects of flow management actions on objectives via environmental flow science. We describe the SDM process and illustrate its application using a case study focused on comparing environmental flow scenarios for the mainstem Willamette River, Oregon, USA. In a short timeframe, SDM was successfully applied to identify management objectives, develop empirical and expert opinion based models predicting ecological responses, and compare scenarios while accounting for uncertainty and partial controllability. We found that no flow scenario was clearly preferred based on available knowledge, largely because river flows could only be partially controlled through available dam operations. Participants agreed that the SDM process was useful and that an additional iteration focused on refining predictive models and incorporating additional objectives could help better inform dam release decisions for the entire basin. In our view, SDM can provide managers with more realistic comparisons of environmental flows by accounting for partial controllability and uncertainty, which may result in greater implementation of available flow management actions.","language":"English","publisher":"Wiley","doi":"10.1111/1752-1688.12845","usgsCitation":"DeWeber, J., and Peterson, J., 2020, Comparing environmental flow implementation options with structured decision making: Case study from the Willamette River, Oregon: Journal of the American Water Resources Association, v. 56, no. 4, p. 599-614, https://doi.org/10.1111/1752-1688.12845.","productDescription":"16 p.","startPage":"599","endPage":"614","ipdsId":"IP-098527","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":395729,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Willamette River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.255615234375,\n 43.23719944365308\n ],\n [\n -123.255615234375,\n 45.62940492064501\n ],\n [\n -121.7449951171875,\n 45.62940492064501\n ],\n [\n -121.7449951171875,\n 43.23719944365308\n ],\n [\n -123.255615234375,\n 43.23719944365308\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"56","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-04-23","publicationStatus":"PW","contributors":{"authors":[{"text":"DeWeber, J. Tyrell","contributorId":275279,"corporation":false,"usgs":false,"family":"DeWeber","given":"J. Tyrell","affiliations":[{"id":25426,"text":"OSU","active":true,"usgs":false}],"preferred":false,"id":833919,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Peterson, James T. 0000-0002-7709-8590 james_peterson@usgs.gov","orcid":"https://orcid.org/0000-0002-7709-8590","contributorId":2111,"corporation":false,"usgs":true,"family":"Peterson","given":"James","email":"james_peterson@usgs.gov","middleInitial":"T.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":833918,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70215561,"text":"70215561 - 2020 - Climate change causes river network contraction and disconnection in the H.J. Andrews Experimental Forest, Oregon, USA","interactions":[],"lastModifiedDate":"2020-10-23T13:58:50.631328","indexId":"70215561","displayToPublicDate":"2020-04-23T08:55:08","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7170,"text":"Frontiers in Water","active":true,"publicationSubtype":{"id":10}},"title":"Climate change causes river network contraction and disconnection in the H.J. Andrews Experimental Forest, Oregon, USA","docAbstract":"Headwater streams account for more than 89% of global river networks and provide numerous ecosystem services that benefit downstream ecosystems and human water uses. It has been established that changes in climate have shifted the timing and magnitude of observed precipitation, which, at specific gages, have been directly linked to long-term reductions in large river discharge. However, climate impacts on ungaged headwater streams, where ecosystem function is tightly coupled to flow permanence along the river corridor, remain unknown due to the lack of data sets and ability to model and predict flow permanence. We analyzed a network of 10 gages with 38–69 years of records across a 5th-order river basin in the U.S. Pacific Northwest, finding increasing frequency of lower low-flow conditions across the basin. Next, we simulated river network expansion and contraction for a 65-year period of record, revealing 24% and 9% declines in flowing and contiguous network length, respectively, during the driest months of the year. This study is the first to mechanistically simulate network expansion and contraction at the scale of a large river basin, informing if and how climate change is altering connectivity along river networks. While the heuristic model presented here yields basin-specific conclusions, this approach is generalizable and transferable to the study of other large river basins. Finally, we interpret our model results in the context of regulations based on flow permanence, demonstrating the complications of static regulatory definitions in the face of non-stationary climate.
We present a comprehensive relative sea-level (RSL) database for north, central, and south-central Chile (18.5°S – 43.6°S) using a consistent, systematic, and internationally comparable approach. Despite its latitudinal extent, this coastline has received little rigorous or systematic attention and details of its RSL history remain largely unexplored. To address this knowledge gap, we re-evaluate the geological context and age of previously published sea-level indicators, providing 78 index points and 84 marine or terrestrial limiting points spanning from 11 ka to the present day. Many data points were originally collected for research in other fields and have not previously been examined for the information they provide on sea-level change. Additionally, we describe new sea-level data from four sites located between the Gulf of Arauco and Valdivia. By compiling RSL histories for 11 different regions, we summarise current knowledge of Chilean RSL. These histories indicate mid Holocene sea levels above present in all regions, but at highly contrasting elevations from ∼30 m to <5 m. We compare the spatiotemporal distribution of sea-level data points with a suite of glacial isostatic adjustment models and place first-order constraints on the influence of tectonic processes over 103–104 year timescales. While seven regions indicate uplift rates <1 m ka−1, the remaining regions may experience substantially higher rates. In addition to enabling discussion of the factors driving sea-level change, our compilation provides a resource to assist attempts to understand the distribution of archaeological, palaeoclimatic, and palaeoseismic evidence in the coastal zone and highlights directions for future sea-level research in Chile.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.quascirev.2020.106281","usgsCitation":"Garrett, E., Melnick, D., Dura, T., Cisternas, M., Ely, L., Wesson, R.L., Jara-Munoz, J., and Whitehouse, P.L., 2020, Holocene relative sea-level change along the tectonically active Chilean coast: Quaternary Science Reviews, v. 236, 106281, 18 p., https://doi.org/10.1016/j.quascirev.2020.106281.","productDescription":"106281, 18 p.","ipdsId":"IP-117621","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":456975,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.quascirev.2020.106281","text":"Publisher Index Page"},{"id":387803,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Chile","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.2890625,\n -41.178653972331674\n ],\n [\n -72.333984375,\n -41.442726377672116\n ],\n [\n -67.939453125,\n -21.943045533438166\n ],\n [\n -73.47656249999999,\n -22.350075806124853\n ],\n [\n -75.673828125,\n -22.350075806124853\n ],\n [\n -76.2890625,\n -41.178653972331674\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"236","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Garrett, Ed","contributorId":263491,"corporation":false,"usgs":false,"family":"Garrett","given":"Ed","email":"","affiliations":[],"preferred":false,"id":820908,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Melnick, Daniel","contributorId":195525,"corporation":false,"usgs":false,"family":"Melnick","given":"Daniel","email":"","affiliations":[],"preferred":false,"id":820909,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dura, Tina","contributorId":195530,"corporation":false,"usgs":false,"family":"Dura","given":"Tina","email":"","affiliations":[{"id":12727,"text":"Rutgers University","active":true,"usgs":false}],"preferred":false,"id":820910,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cisternas, Marco","contributorId":198928,"corporation":false,"usgs":false,"family":"Cisternas","given":"Marco","affiliations":[],"preferred":false,"id":820911,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ely, Lisa","contributorId":195528,"corporation":false,"usgs":false,"family":"Ely","given":"Lisa","affiliations":[],"preferred":false,"id":820912,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wesson, Robert L. 0000-0003-2702-0012 rwesson@usgs.gov","orcid":"https://orcid.org/0000-0003-2702-0012","contributorId":850,"corporation":false,"usgs":true,"family":"Wesson","given":"Robert","email":"rwesson@usgs.gov","middleInitial":"L.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":820913,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jara-Munoz, Julius","contributorId":263474,"corporation":false,"usgs":false,"family":"Jara-Munoz","given":"Julius","affiliations":[{"id":53996,"text":"Department of Earth and Environmental Sciences, University of Potsdam, Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":820914,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Whitehouse, Pippa L","contributorId":263475,"corporation":false,"usgs":false,"family":"Whitehouse","given":"Pippa","email":"","middleInitial":"L","affiliations":[{"id":53998,"text":"Department of Geography, Durham University, Durham, UK","active":true,"usgs":false}],"preferred":false,"id":820915,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70228656,"text":"70228656 - 2020 - Assessment of spatial genetic structure to identify populations at risk for infection of an emerging epizootic disease","interactions":[],"lastModifiedDate":"2022-02-16T17:55:01.617963","indexId":"70228656","displayToPublicDate":"2020-04-22T11:48:13","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Assessment of spatial genetic structure to identify populations at risk for infection of an emerging epizootic disease","docAbstract":"Salt marshes provide the important ecosystem service of carbon storage in their sediments; however, little is known about the sources of such carbon and whether they differ between historically unaltered and restoring systems. In this study, stable isotope analysis was used to quantify carbon sources in a restoring, sparsely vegetated marsh (Restoring) and an adjacent, historically unaltered marsh (Reference) in the Nisqually River Delta (NRD) of Washington, USA. Three sediment cores were collected at “Inland” and “Seaward” locations at both marshes ~ 6 years after restoration. Benthic diatoms, C3 plants, C4 plants, and particulate organic matter (POM) were collected throughout the NRD. δ13C and δ15N values of sources and sediments were used in a Bayesian stable isotope mixing model to determine the contribution of each carbon source to the sediments of both marshes. Autochthonous marsh C3 plants contributed 73 ± 10% (98 g C m−2 year−1) and 89 ± 11% (119 g C m−2 year−1) to Reference-Inland and Reference-Seaward sediment carbon sinks, respectively. In contrast, the sediment carbon sink at the Restoring Marsh received a broad assortment of predominantly allochthonous materials, which varied in relative contribution based on source distance and abundance. Marsh POM contributed the most to Restoring-Seaward (42 ± 34%) (69 g C m−2 year−1) followed by Riverine POM at Restoring-Inland (32 ± 41%) (52 g C m−2 year−1). Overall, this study demonstrates that largely unvegetated, restoring marshes can accumulate carbon by relying predominantly on allochthonous material, which comes mainly from the most abundant and closest estuarine sources.
ces.
","language":"English","publisher":"Springer","doi":"10.1007/s12237-020-00748-7","usgsCitation":"Drexler, J.Z., Davis, M.J., Woo, I., and De La Cruz, S.E., 2020, Carbon sources in the sediments of a restoring vs. historically unaltered salt marsh: Estuaries and Coasts, v. 43, p. 1345-1360, https://doi.org/10.1007/s12237-020-00748-7.","productDescription":"16 p.","startPage":"1345","endPage":"1360","ipdsId":"IP-109595","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":456984,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s12237-020-00748-7","text":"Publisher Index Page"},{"id":376842,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Nisqually River Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.3819580078125,\n 46.68713141244413\n ],\n [\n -122.0855712890625,\n 46.68713141244413\n ],\n [\n -122.0855712890625,\n 47.51349065484327\n ],\n [\n -123.3819580078125,\n 47.51349065484327\n ],\n [\n -123.3819580078125,\n 46.68713141244413\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"43","noUsgsAuthors":false,"publicationDate":"2020-04-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Drexler, Judith Z. 0000-0002-0127-3866 jdrexler@usgs.gov","orcid":"https://orcid.org/0000-0002-0127-3866","contributorId":167492,"corporation":false,"usgs":true,"family":"Drexler","given":"Judith","email":"jdrexler@usgs.gov","middleInitial":"Z.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":794369,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Davis, Melanie J. 0000-0003-1734-7177","orcid":"https://orcid.org/0000-0003-1734-7177","contributorId":202773,"corporation":false,"usgs":true,"family":"Davis","given":"Melanie","email":"","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":794370,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Woo, Isa 0000-0002-8447-9236 iwoo@usgs.gov","orcid":"https://orcid.org/0000-0002-8447-9236","contributorId":2524,"corporation":false,"usgs":true,"family":"Woo","given":"Isa","email":"iwoo@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":794371,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"De La Cruz, Susan E.W. 0000-0001-6315-0864 sdelacruz@usgs.gov","orcid":"https://orcid.org/0000-0001-6315-0864","contributorId":3248,"corporation":false,"usgs":true,"family":"De La Cruz","given":"Susan","email":"sdelacruz@usgs.gov","middleInitial":"E.W.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":794372,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70217223,"text":"70217223 - 2020 - The Missoula and Bonneville floods—A review of ice-age megafloods in the Columbia River basin","interactions":[],"lastModifiedDate":"2021-01-13T13:59:28.598323","indexId":"70217223","displayToPublicDate":"2020-04-22T07:51:58","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1431,"text":"Earth-Science Reviews","active":true,"publicationSubtype":{"id":10}},"title":"The Missoula and Bonneville floods—A review of ice-age megafloods in the Columbia River basin","docAbstract":"The Channeled Scabland of eastern Washington State, USA, brought megafloods to the scientific forefront. A 30,000-km2 landscape of coulees and cataracts carved into the region’s loess-covered basalt attests to overwhelming volumes of energetic water. The scarred landscape, garnished by huge boulder bars and far-travelled ice-rafted erratics, spurred J Harlen Bretz’s vigorously disputed flood hypothesis in the 1920s. First known as the Spokane flood, it was rebranded the Missoula flood once understood that the water came from glacial Lake Missoula, formed when the Purcell Trench lobe of the last-glacial Cordilleran ice sheet dammed the Clark Fork valley in northwestern Idaho with ice a kilometer thick. Bretz’s flood evidence in the once-remote Channeled Scabland, widely seen and elaborated by the 1950s, eventually swayed consensus for cataclysmic flooding. Missoula flood questions then turned to some that continue today: how many? when? how big? what routes? what processes?
The Missoula floods passed through eastern Washington by a multitude of valleys, coulees and scabland tracts, some contemporaneously, some sequentially. Which routings and their timing depended on the positions of various lobes of the multi-pronged Cordilleran ice sheet and the erosional development of the channels themselves. The first floods mostly followed the big bend of Columbia valley looping through north-central Washington. But the south-advancing Okanogan ice lobe soon blocked that path, forming long-lasting glacial Lake Columbia in the impounded Columbia valley. Missoula floods into this lake were diverted south out of the Columbia valley and into eastern Washington coulees and scabland tracts. At least four floods entered Moses Coulee, but then as the Okanogan lobe advanced over and blocked the head of that coulee, more eastern paths took the water, including Grand Coulee and the Telford-Crab-Creek and Cheney-Palouse scabland tracts. Flood routing also depended on the erosion of the coulees. At some point, headward erosion of upper Grand Coulee lowered the divide saddle between the west-running Columbia valley and the deep and wide Grand Coulee heading southwest. Still uncertain is when this happened and the consequences with respect to the stage and extent of glacial Lake Columbia and to flood access to the other, higher, flood routes. Downstream, all flood routes converged onto Pasco Basin, flowed through Wallula Gap and the Columbia River Gorge into the Pacific Ocean, following submarine canyons and depositing sediment layers on abyssal plains.
Stratigraphic studies indicate dozens—likely more than a hundred—separate Missoula floods during the last glacial period. Over the length of the flood route, backwater areas and depositional basins preserve multiple flood beds, many of which are separated by signs of time, including volcanic ash layers and soil development in subaerial environments; and varve-like beds and pelagic mud layers in lacustrine and marine settings. Evidence also comes from the glacial Lake Missoula basin, where stratigraphy indicates dozens of filling and emptying cycles. Varve counts in conjunction of radiocarbon dating and paleomagnetic secular variation show the repeated filling-and-release cycles of glacial Lake Missoula had intervals possibly as long as 100 years early in the lake’s history but diminished to just one or two years for the last few floods. This behavior accords with jökulhlaup-style floods released by subglacial drainage from a self-dumping ice-dammed lake. But not yet clear is whether such a mechanism applies to all the floods or if some emptied more cataclysmically as hypothesized by some.
Radiocarbon dating of sparse organic materials remains key to defining flood chronology but has been lately bolstered by analyses of terrestrial cosmogenic nuclides and optically stimulated luminescence. Varve counts and paleomagnetic secular variation studies help to define durations and intervals represented by sequences of flood beds. The ~16 ka Mount St. Helens Set S tephra is commonly interbedded within flood deposits, enabling correlation of deposits among sites. Tephra from the 13.7–13.4 ka eruption of Glacier Peak overlies all glacial Lake Missoula and Missoula flood deposits, defining an end time. Overall conclusions are that glacial Lake Missoula was extant and producing floods for at least 3–4 ky during 20–14 ka. At least ~75 floods preceded Mount St Helens Set S, followed by 30 or more after the tephra fall. Most floods entered glacial Lake Columbia, impounded by the Okanogan lobe, for 2–5 ky between about 18.5 and 15 ka. Glacial Lake Columbia outlived Lake Missoula by >200–400 yr but may have been born later since at least one flood came down the Columbia valley before the Okanogan ice lobe blocked the Columbia valley at 18.5–18 ka. The maximum extent of the Okanogan and Purcell Trench lobes, many Missoula floods, substantial erosion of upper Grand Coulee, and the widespread tephra falls from Mount St. Helens eruptions all happened about 17–15 ka. People, in the area since 16.6–15.3 ka, almost certainly witnessed the last of the Missoula floods and later large floods from other ice-dammed lakes in the Columbia River basin.
Quantitative flow analyses give peak discharge estimates and support understanding of erosional and depositional processes. The first flow assessments were simple cross-section calculations but recent assessments employ two-dimensional hydrodynamic models. The general finding is that emplacement of the maximum stage evidence requires about 20 million m3/s near the Lake Missoula outlet and about 5–15 million m3/s through Wallula Gap and downstream in the Columbia River Gorge. These hydraulic analyses raise still-unresolved questions regarding canyon erosion and possible additional water sources.
The large Pleistocene Bonneville flood entered the Columbia River system from the southeast from pluvial Lake Bonneville, the Pleistocene predecessor to Great Salt Lake in the eastern Great Basin. During the last glacial, the lake basin filled, covering >50,000 km2 with 10,400 km3 of water before reaching its maximum possible stage governed by Red Rock Pass, the lowest divide separating the basin from the Snake River basin to the north. The overtopping lake rapidly incised 108–125 m into the Red Rock Pass outlet, spilling half of its total lake volume. G.K. Gilbert described the essential sequence in the 1870s, but the flood was mostly forgotten until the late 1950s when Harold Malde linked the spectacular scabland topography and bouldery “melon gravel” on the Snake River Plain to the Lake Bonneville overflow. The Bonneville flood appears to have been a singular event at about 18 ka. No evidence of multiple or pre-last-glacial spillovers has yet been found. Its total volume was about twice that of a maximum Lake Missoula flood yet its peak discharge was ~1 million m3/s, less than a tenth of the largest Missoula floods. Its comparatively simple flow path and much steadier flow make the Bonneville flood ideal for new studies of erosional and depositional processes.
At least two floods seem to have passed down the Columbia valley after the last of the Missoula floods, including a large flood about ~14 ka likely from cataclysmic demise of the thinning Okanogan ice lobe dam impounding glacial Lake Columbia. Floods from earlier glacial ages left scant yet clear evidence in the Channeled Scabland and Columbia valley. But their source, timing, and magnitudes are little understood. Some deposits are paleomagnetically reversed, thus older than ~800 ka. Last-glacial floods and perhaps older ones affected the Snake River Plain, some likely sourced in lakes dammed by alpine glaciers in central Idaho.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.earscirev.2020.103181","usgsCitation":"O'Connor, J., Baker, V.R., Waitt, R.B., Smith, L.N., Cannon, C.M., George, D.L., and Denlinger, R.P., 2020, The Missoula and Bonneville floods—A review of ice-age megafloods in the Columbia River basin: Earth-Science Reviews, v. 208, 103181, 51 p., https://doi.org/10.1016/j.earscirev.2020.103181.","productDescription":"103181, 51 p.","ipdsId":"IP-117652","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":456992,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://archimer.ifremer.fr/doc/00624/73634/","text":"External Repository"},{"id":382128,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Oregon, Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.76171875,\n 45.73685954736049\n ],\n [\n -116.4111328125,\n 45.73685954736049\n ],\n [\n -116.4111328125,\n 48.31242790407178\n ],\n [\n -120.76171875,\n 48.31242790407178\n ],\n [\n -120.76171875,\n 45.73685954736049\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"208","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"O'Connor, Jim E. 0000-0002-7928-5883 oconnor@usgs.gov","orcid":"https://orcid.org/0000-0002-7928-5883","contributorId":140771,"corporation":false,"usgs":true,"family":"O'Connor","given":"Jim E.","email":"oconnor@usgs.gov","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":false,"id":808089,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baker, Victor R.","contributorId":201141,"corporation":false,"usgs":false,"family":"Baker","given":"Victor","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":808090,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Waitt, Richard B. 0000-0002-6392-5604 waitt@usgs.gov","orcid":"https://orcid.org/0000-0002-6392-5604","contributorId":2343,"corporation":false,"usgs":true,"family":"Waitt","given":"Richard","email":"waitt@usgs.gov","middleInitial":"B.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":808091,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, Larry N","contributorId":247679,"corporation":false,"usgs":false,"family":"Smith","given":"Larry","email":"","middleInitial":"N","affiliations":[{"id":49605,"text":"Montana Technological University","active":true,"usgs":false}],"preferred":false,"id":808092,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cannon, Charles M. 0000-0003-4136-2350 ccannon@usgs.gov","orcid":"https://orcid.org/0000-0003-4136-2350","contributorId":247680,"corporation":false,"usgs":true,"family":"Cannon","given":"Charles","email":"ccannon@usgs.gov","middleInitial":"M.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":808093,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"George, David L. 0000-0002-5726-0255 dgeorge@usgs.gov","orcid":"https://orcid.org/0000-0002-5726-0255","contributorId":3120,"corporation":false,"usgs":true,"family":"George","given":"David","email":"dgeorge@usgs.gov","middleInitial":"L.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":808094,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Denlinger, Roger P. 0000-0003-0930-0635 roger@usgs.gov","orcid":"https://orcid.org/0000-0003-0930-0635","contributorId":2679,"corporation":false,"usgs":true,"family":"Denlinger","given":"Roger","email":"roger@usgs.gov","middleInitial":"P.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true}],"preferred":true,"id":808095,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70209671,"text":"ofr20201023 - 2020 - Design and methods of the California stream quality assessment (CSQA), 2017","interactions":[],"lastModifiedDate":"2020-04-27T12:01:20.795249","indexId":"ofr20201023","displayToPublicDate":"2020-04-21T14:01:19","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1023","displayTitle":"Design and Methods of the California Stream Quality Assessment (CSQA), 2017","title":"Design and methods of the California stream quality assessment (CSQA), 2017","docAbstract":"During 2017, as part of the National Water-Quality Assessment Project, the U.S. Geological Survey conducted the California Stream Quality Assessment to investigate the quality of streams in the Central California Foothills and Coastal Mountains ecoregion, United States. The goal of the California Stream Quality Assessment study was to assess the health of wadeable streams in the region by characterizing multiple water-quality factors that are stressors to aquatic biota and by evaluating the relation between these stressors and biological indicators of stream health. Urbanization, agriculture, and modifications to streamflow are anthropogenic changes that affect water quality in the region; consequently, the study design primarily targeted sites and specific stressors associated with these activities. For the study, 85 stream sites were selected to represent the types and intensity of land use in the watershed; categories of site types were undeveloped, urban (low, medium, high), agriculture (low, high), and mixed (urban and agriculture). Most sites (about 70 percent) represent a gradient of urbanization from undeveloped to 99-percent urbanized. At most of the sites, streamgages or pressure transducers were used to monitor stream discharge and stage, as well as temperature. Water-quality samples were collected routinely at all sites and were analyzed for major ions, organic contaminants, nutrients, and suspended sediment. Sampling frequency varied on the basis of site type and location. Discrete water samples were collected weekly and generally 6 times per site, except for 11 undeveloped sites that were sampled only 4 times (during the last 4 weeks). Water sampling began at sites in the southern part of the study on March 13, 2017, and at sites in the northern part of the study on April 3, 2017. Passive samplers were deployed at most sites for measurement of polar organic contaminants (pesticides and pharmaceuticals). In May 2017, coincident with completion of water-quality sampling, an ecological survey was conducted at each site to assess benthic algal and macroinvertebrate communities and instream habitat. During the ecological surveys, a single composite streambed-sediment sample was collected for chemical analysis and toxicity testing. In addition, a few focused studies were done at subsets of sites, namely, measuring pesticides using small-volume automated samplers, measuring pesticides in biofilms, and sampling suspended sediments using passive samplers. This report describes the various study components and methods of the California Stream Quality Assessment, including measurements of water quality, sediment chemistry, habitat assessments, and ecological surveys, as well as procedures for sample analysis, quality assurance and quality control, and data management.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201023","collaboration":"National Water Quality Program","usgsCitation":"May, J.T., Nowell, L.H., Coles, J.F., Button, D.T., Bell, A.H., Qi, S.L., and Van Metre, P.C., 2020, Design and methods of the California stream quality assessment, 2017: U.S. Geological Survey Open-File Report 2020–1023, 88 p.,","productDescription":"Report: x, 88 p.; 1 Table","numberOfPages":"88","onlineOnly":"Y","ipdsId":"IP-105445","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":374128,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1023/ofr20201023.pdf","text":"Report","size":"6 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":374127,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1023/coverthb.jpg"},{"id":374197,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2020/1023/ofr20201023_app_table_1.1.xlsx","text":"Table 1.1","size":"30 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":" - Sampling matrix for the 85 sites used in the U.S. Geological Survey California Stream Quality Assessment in 2017."}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.34423828125,\n 34.50655662164561\n ],\n [\n -119.267578125,\n 34.95799531086792\n ],\n [\n -121.4208984375,\n 37.78808138412046\n ],\n [\n -122.10205078125,\n 39.14710270770074\n ],\n [\n -122.25585937500001,\n 39.41922073655956\n ],\n [\n -123.48632812499999,\n 38.85682013474361\n ],\n [\n -122.67333984374999,\n 37.87485339352928\n ],\n [\n -122.05810546875,\n 37.055177106660814\n ],\n [\n -121.79443359375,\n 36.65079252503471\n ],\n [\n -121.9482421875,\n 36.61552763134925\n ],\n [\n -121.83837890625,\n 36.155617833818525\n ],\n [\n -121.26708984374999,\n 35.585851593232356\n ],\n [\n -120.58593749999999,\n 35.04798673426734\n ],\n [\n -120.5419921875,\n 34.488447837809304\n ],\n [\n -120.34423828125,\n 34.50655662164561\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Background
Biological controls with predators of larval mosquito vectors have historically focused almost exclusively on insectivorous animals, with few studies examining predatory plants as potential larvacidal agents. In this study, we experimentally evaluate a generalist plant predator of North America, Utricularia macrorhiza, the common bladderwort, and evaluate its larvacidal efficiency for the mosquito vectors Aedes aegypti and Aedes albopictus in no-choice, laboratory experiments. We sought to determine first, whether U. macrorhiza is a competent predator of container-breeding mosquitoes, and second, its predation efficiency for early and late instar larvae of each mosquito species.
Methods
Newly hatched, first instar Aedes albopictus and Aedes aegypti larvae were separately exposed in cohorts of 10 to field collected U. macrorhiza cuttings. Data on development time and larval survival were collected on a daily basis to ascertain the effectiveness of U. macrorhiza as a larval predator. Survival models were used to assess differences in larval survival between cohorts that were exposed to U. macrorhiza and those that were not. A permutation analysis was used to investigate whether storing U. macrorhiza in laboratory conditions for extended periods of time (1 month vs. 6 months) affected its predation efficiency.
Results
Our results indicated a 100% and 95% reduction of survival of Ae. aegypti and Ae. albopictus larvae respectively, in the presence of U. macrorhiza relative to controls within five days, with peak larvacidal efficiency in plant cuttings from ponds collected in August. Utricularia macrorhiza cuttings, which were prey-deprived, and maintained in laboratory conditions for 6 months were more effective larval predators than cuttings, which were maintained prey-free for 1 month.
Conclusions
Due to the combination of high predation efficiency and the unique biological feature of facultative predation, we suggest that U. macrorhiza warrants further development as a method for larval mosquito control.
The Arabian Peninsula borders the hottest reefs in the world, and corals living in these extreme environments can provide insight into the effects of warming on coral health and disease. Here, we examined coral reef health at 17 sites across three regions along the northeastern Arabian Peninsula (Persian Gulf, Strait of Hormuz and Oman Sea) representing a gradient of environmental conditions. The Persian Gulf has extreme seasonal fluctuations in temperature and chronic hypersalinity, whereas the other two regions experience more moderate conditions. Field surveys identified 13 coral diseases including tissue loss diseases of unknown etiology (white syndromes) in Porites, Platygyra, Dipsastraea, Cyphastrea, Acropora and Goniopora; growth anomalies in Porites, Platygyra and Dipsastraea; black band disease in Platygyra, Dipsastraea, Acropora, Echinopora and Pavona; bleached patches in Porites and Goniopora and a disease unique to this region, yellow-banded tissue loss in Porites. The most widespread diseases were Platygyra growth anomalies (52.9% of all surveys), Acropora white syndrome (47.1%) and Porites bleached patches (35.3%). We found a number of diseases not yet reported in this region and found differential disease susceptibility among coral taxa. Disease prevalence was higher on reefs within the Persian Gulf (avg. 2.05%) as compared to reefs within the Strait of Hormuz (0.46%) or Oman Sea (0.25%). A high number of localized disease outbreaks (8 of 17 sites) were found, especially within the Persian Gulf (5 of 8 sites). Across all regions, the majority of variation in disease prevalence (82.2%) was associated with the extreme temperature range experienced by these corals combined with measures of organic pollution and proximity to shore. Thermal stress is known to drive a number of coral diseases, and thus, this region provides a platform to study disease at the edge of corals’ thermal range.
","language":"English","publisher":"Springer","doi":"10.1007/s00338-020-01928-4","usgsCitation":"Aeby, G.S., Howells, E., Work, T.M., Abrego, D., Williams, G.J., Wedding, L.M., Caldwell, J.M., Moritsch, M.M., and Burt, J., 2020, Localized outbreaks of coral disease on Arabian reefs are linked to extreme temperatures and environmental stressors: Coral Reefs, v. 39, p. 829-846, https://doi.org/10.1007/s00338-020-01928-4.","productDescription":"18 p.","startPage":"829","endPage":"846","ipdsId":"IP-117756","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":457021,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s00338-020-01928-4","text":"Publisher Index Page"},{"id":378662,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Oman, United Arab Emirates","otherGeospatial":"Gulf of Oman, Persian Gulf, Strait of Hormuz","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 51.8115234375,\n 23.765236889758672\n ],\n [\n 56.953125,\n 23.765236889758672\n ],\n [\n 56.953125,\n 26.667095801104814\n ],\n [\n 51.8115234375,\n 26.667095801104814\n ],\n [\n 51.8115234375,\n 23.765236889758672\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"39","noUsgsAuthors":false,"publicationDate":"2020-04-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Aeby, Greta S.","contributorId":64783,"corporation":false,"usgs":false,"family":"Aeby","given":"Greta","email":"","middleInitial":"S.","affiliations":[{"id":13394,"text":"Hawai‘i Institute of Marine Biology","active":true,"usgs":false}],"preferred":false,"id":799354,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Howells, Emily","contributorId":225444,"corporation":false,"usgs":false,"family":"Howells","given":"Emily","email":"","affiliations":[{"id":41112,"text":"Center for Genomics and Systems Biology, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates","active":true,"usgs":false}],"preferred":false,"id":799355,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Work, Thierry M. 0000-0002-4426-9090 thierry_work@usgs.gov","orcid":"https://orcid.org/0000-0002-4426-9090","contributorId":1187,"corporation":false,"usgs":true,"family":"Work","given":"Thierry","email":"thierry_work@usgs.gov","middleInitial":"M.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":799356,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Abrego, David","contributorId":225447,"corporation":false,"usgs":false,"family":"Abrego","given":"David","email":"","affiliations":[{"id":41113,"text":"Department of Natural Science and Public Health, Zayed University, Abu Dhabi, United Arab Emirates","active":true,"usgs":false}],"preferred":false,"id":799357,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Williams, Gareth J.","contributorId":47898,"corporation":false,"usgs":true,"family":"Williams","given":"Gareth","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":799358,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wedding, Lisa M.","contributorId":241019,"corporation":false,"usgs":false,"family":"Wedding","given":"Lisa","email":"","middleInitial":"M.","affiliations":[{"id":25447,"text":"University of Oxford","active":true,"usgs":false}],"preferred":false,"id":799359,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Caldwell, Jamie M.","contributorId":241020,"corporation":false,"usgs":false,"family":"Caldwell","given":"Jamie","email":"","middleInitial":"M.","affiliations":[{"id":36402,"text":"University of Hawaii","active":true,"usgs":false}],"preferred":false,"id":799360,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Moritsch, Monica M","contributorId":149980,"corporation":false,"usgs":false,"family":"Moritsch","given":"Monica","email":"","middleInitial":"M","affiliations":[{"id":17873,"text":"Department of Ecology and Evolutionary Biology, University of California, Santa Cruz CA 95060","active":true,"usgs":false}],"preferred":false,"id":799361,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Burt, John","contributorId":225446,"corporation":false,"usgs":false,"family":"Burt","given":"John","email":"","affiliations":[{"id":41112,"text":"Center for Genomics and Systems Biology, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates","active":true,"usgs":false}],"preferred":false,"id":799362,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70210710,"text":"70210710 - 2020 - Acoustic Sediment Estimation Toolbox (ASET): A software package for calibrating and processing TRDI ADCP data to compute suspended-sediment transport in sandy rivers","interactions":[],"lastModifiedDate":"2020-06-18T14:36:43.859337","indexId":"70210710","displayToPublicDate":"2020-04-20T09:30:06","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1315,"text":"Computers & Geosciences","printIssn":"0098-3004","active":true,"publicationSubtype":{"id":10}},"title":"Acoustic Sediment Estimation Toolbox (ASET): A software package for calibrating and processing TRDI ADCP data to compute suspended-sediment transport in sandy rivers","docAbstract":"Quantifying suspended-sediment transport is critical for a variety of disciplines related to the management of water resources. However, the number of gauging stations and monitoring networks in most rivers around the world is insufficient to improve understanding of river dynamics and support water resource management decisions. This is mainly due to the high operational costs and intensive labor involved in traditional sediment measurement techniques, especially in sand bed rivers where coarse material varies spatially in the river cross section. Recently, the acoustic surrogate method has received attention as a potentially accurate surrogate technology for estimating suspended-sediment concentrations. In addition, the acoustic surrogate method, through use of acoustic Doppler current profilers (ADCPs), has the advantage of being able to simultaneously measure the flow velocity field and cross-sectional area when moving-boat measurements are performed. In spite of the important advances made in the implementation of this technique, there are no widely-available, free tools for processing the ADCP acoustic signal cross section measurements which include options to extrapolate velocity and sediment in unmeasured ADCP zones and develop calibrations with physical samples. This paper presents a new software called Acoustic Sediment Estimation Toolbox (ASET), which enables the user to develop a calibration between the acoustic signal collected with a down-looking Teledyne RD Instruments ADCP and sediment concentrations determined using traditional sediment sampling techniques. Moreover, ASET software uses dynamic ADCP measurements to estimate the total suspended-sediment transport through a river cross section. The theoretical framework and data processing routines applied by each module in ASET are presented. Finally, a comparison is made between the results obtained by ASET and by traditional methodologies for computing suspended-sediment transport in a large river system (Paraná River, Argentina).","language":"English","publisher":"Elsevier","doi":"10.1016/j.cageo.2020.104499","usgsCitation":"Dominguez Ruben, L.G., Szupiany, R., Latosinski, F., Lopez Weibel, C., Wood, M.S., and Boldt, J.A., 2020, Acoustic Sediment Estimation Toolbox (ASET): A software package for calibrating and processing TRDI ADCP data to compute suspended-sediment transport in sandy rivers: Computers & Geosciences, v. 140, https://doi.org/10.1016/j.cageo.2020.104499.","productDescription":"104499, 13 p.","startPage":"Article 104499","ipdsId":"IP-102073","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":375680,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Argentina","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[-65.5,-55.2],[-66.45,-55.25],[-66.95992,-54.89681],[-67.56244,-54.87001],[-68.63335,-54.8695],[-68.63401,-52.63637],[-68.25,-53.1],[-67.75,-53.85],[-66.45,-54.45],[-65.05,-54.7],[-65.5,-55.2]]],[[[-64.96489,-22.07586],[-64.37702,-22.79809],[-63.98684,-21.99364],[-62.84647,-22.03499],[-62.68506,-22.24903],[-60.84656,-23.88071],[-60.02897,-24.0328],[-58.80713,-24.77146],[-57.77722,-25.16234],[-57.63366,-25.60366],[-58.61817,-27.12372],[-57.60976,-27.3959],[-56.4867,-27.5485],[-55.69585,-27.38784],[-54.78879,-26.62179],[-54.62529,-25.73926],[-54.13005,-25.54764],[-53.62835,-26.12487],[-53.64874,-26.92347],[-54.49073,-27.47476],[-55.16229,-27.88192],[-56.2909,-28.85276],[-57.62513,-30.21629],[-57.87494,-31.01656],[-58.14244,-32.0445],[-58.13265,-33.04057],[-58.34961,-33.26319],[-58.42707,-33.90945],[-58.49544,-34.43149],[-57.22583,-35.28803],[-57.36236,-35.97739],[-56.73749,-36.41313],[-56.78829,-36.90157],[-57.74916,-38.18387],[-59.23186,-38.72022],[-61.23745,-38.92842],[-62.33596,-38.82771],[-62.12576,-39.4241],[-62.33053,-40.17259],[-62.14599,-40.6769],[-62.7458,-41.02876],[-63.77049,-41.16679],[-64.73209,-40.80268],[-65.11804,-41.06431],[-64.97856,-42.058],[-64.30341,-42.35902],[-63.75595,-42.04369],[-63.45806,-42.56314],[-64.3788,-42.87356],[-65.1818,-43.49538],[-65.32882,-44.50137],[-65.56527,-45.03679],[-66.50997,-45.03963],[-67.29379,-45.5519],[-67.58055,-46.30177],[-66.59707,-47.03392],[-65.64103,-47.23613],[-65.98509,-48.13329],[-67.16618,-48.69734],[-67.81609,-49.86967],[-68.72875,-50.26422],[-69.13854,-50.73251],[-68.81556,-51.7711],[-68.14999,-52.34998],[-68.57155,-52.29944],[-69.49836,-52.14276],[-71.9148,-52.00902],[-72.3294,-51.42596],[-72.30997,-50.67701],[-72.97575,-50.74145],[-73.32805,-50.37879],[-73.41544,-49.31844],[-72.64825,-48.87862],[-72.33116,-48.24424],[-72.44736,-47.73853],[-71.91726,-46.88484],[-71.55201,-45.56073],[-71.65932,-44.97369],[-71.22278,-44.78424],[-71.3298,-44.40752],[-71.79362,-44.20717],[-71.46406,-43.78761],[-71.91542,-43.40856],[-72.1489,-42.25489],[-71.7468,-42.05139],[-71.91573,-40.83234],[-71.68076,-39.80816],[-71.41352,-38.91602],[-70.81466,-38.553],[-71.11863,-37.57683],[-71.12188,-36.65812],[-70.36477,-36.00509],[-70.38805,-35.16969],[-69.81731,-34.19357],[-69.81478,-33.27389],[-70.0744,-33.09121],[-70.53507,-31.36501],[-69.91901,-30.33634],[-70.01355,-29.36792],[-69.65613,-28.45914],[-69.00123,-27.52121],[-68.29554,-26.89934],[-68.5948,-26.50691],[-68.386,-26.18502],[-68.41765,-24.51855],[-67.32844,-24.0253],[-66.98523,-22.98635],[-67.10667,-22.73592],[-66.27334,-21.83231],[-64.96489,-22.07586]]]]},\"properties\":{\"name\":\"Argentina\"}}]}","volume":"140","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Dominguez Ruben, Lucas Gerardo","contributorId":225404,"corporation":false,"usgs":false,"family":"Dominguez Ruben","given":"Lucas","email":"","middleInitial":"Gerardo","affiliations":[{"id":41098,"text":"Littoral National University, Argentina","active":true,"usgs":false}],"preferred":false,"id":791058,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Szupiany, Ricardo","contributorId":225405,"corporation":false,"usgs":false,"family":"Szupiany","given":"Ricardo","affiliations":[{"id":41098,"text":"Littoral National University, Argentina","active":true,"usgs":false}],"preferred":false,"id":791059,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Latosinski, Francisco","contributorId":225406,"corporation":false,"usgs":false,"family":"Latosinski","given":"Francisco","affiliations":[{"id":41099,"text":"National Scientific and Technical Research Council, Argentina","active":true,"usgs":false}],"preferred":false,"id":791060,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lopez Weibel, Cecilia","contributorId":225407,"corporation":false,"usgs":false,"family":"Lopez Weibel","given":"Cecilia","affiliations":[{"id":32938,"text":"Littoral National University","active":true,"usgs":false}],"preferred":false,"id":791061,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wood, Molly S. 0000-0002-5184-8306 mswood@usgs.gov","orcid":"https://orcid.org/0000-0002-5184-8306","contributorId":788,"corporation":false,"usgs":true,"family":"Wood","given":"Molly","email":"mswood@usgs.gov","middleInitial":"S.","affiliations":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true},{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true},{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"preferred":true,"id":791062,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Boldt, Justin A. 0000-0002-0771-3658","orcid":"https://orcid.org/0000-0002-0771-3658","contributorId":207849,"corporation":false,"usgs":true,"family":"Boldt","given":"Justin","email":"","middleInitial":"A.","affiliations":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true},{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true}],"preferred":true,"id":791063,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70228074,"text":"70228074 - 2020 - Identifying candidate reference reaches to assess the physical and biological integrity of wadeable streams in different ecoregions and among stream sizes","interactions":[],"lastModifiedDate":"2022-02-16T14:39:39.755202","indexId":"70228074","displayToPublicDate":"2020-04-20T08:32:06","publicationYear":"2020","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":"Identifying candidate reference reaches to assess the physical and biological integrity of wadeable streams in different ecoregions and among stream sizes","docAbstract":"Efforts to quantify disturbances to aquatic systems often use landscape-level metrics, presumably linked to ecological integrity, but fewer studies have directly linked ecological integrity to instream habitat, and applied these results to unsampled stream reaches throughout a landscape. We developed a flexible, quantitative approach that characterizes stream impairment across a landscape and identifies least-disturbed stream reaches to serve as benchmarks for high quality physical habitat and ecological integrity. Fish and macroinvertebrate community characteristics, reach-level physical habitat and water quality metrics were summarized in 891 wadeable stream reaches across two ecoregions in Missouri, USA. The influence of reach and water-quality characteristics as well as landscape-level variables on 7 fish and 3 macroinvertebrate community biological indicator metrics was then modeled using boosted regression trees (BRTs). On average, reach-level models explained more variance (25 and 27% in the two ecoregions examined) in biotic metrics than landscape-level models (18% and 20%). Abiotic and biotic associations differed among ecoregions and stream sizes, however, reach-level habitat (e.g., bankfull width/depth ratio, channel incision height) and water quality (e.g., dissolved oxygen, total chlorophyll) were consistently top predictors. At the landscape scale, fish richness in the agriculturally dominated ecoregion increased with decreased fragmentation/flow modification, while invertebrate metrics responded to agricultural disturbances. Invertebrate metrics in the forested ecoregion showed community degradation apparent with crop coverage as low as 8-10% of the riparian zone, while urban impairment was best detected using invertebrate indicators of biotic integrity and measures of fish trophic ecology. Relationships among landscape-scale variables and reach characteristics identified as top predictors in BRTs also highlighted potential mechanistic relationships among landscape, habitat, and measures of ecological integrity. Using the results of the landscape-level models, estimates for overall ecological integrity were predicted for over 28,000 stream reaches throughout Missouri, and a total of 1,423 candidate reference reaches were identified. The objective approach to characterizing stream impairment developed in this study offers specific advantages, including a reach and landscape-level evaluation of human disturbance as well as an inductive, multi-metric determination of ecological integrity.","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2019.105966","usgsCitation":"Paukert, C.P., Kleeklamp, E.R., and Tingley, R.W., 2020, Identifying candidate reference reaches to assess the physical and biological integrity of wadeable streams in different ecoregions and among stream sizes: Ecological Indicators, v. 111, 105966, 15 p., https://doi.org/10.1016/j.ecolind.2019.105966.","productDescription":"105966, 15 p.","ipdsId":"IP-097233","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":395344,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"111","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Paukert, Craig P. 0000-0002-9369-8545","orcid":"https://orcid.org/0000-0002-9369-8545","contributorId":245524,"corporation":false,"usgs":true,"family":"Paukert","given":"Craig","middleInitial":"P.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":833017,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kleeklamp, Ethan R.","contributorId":274478,"corporation":false,"usgs":false,"family":"Kleeklamp","given":"Ethan","email":"","middleInitial":"R.","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":833018,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tingley, Ralph William 0000-0002-1689-2133","orcid":"https://orcid.org/0000-0002-1689-2133","contributorId":258043,"corporation":false,"usgs":true,"family":"Tingley","given":"Ralph","email":"","middleInitial":"William","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":833062,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70209677,"text":"70209677 - 2020 - A critical assessment of human-impact indices based on anthropogenic pollen indicators","interactions":[],"lastModifiedDate":"2020-04-21T14:49:32.79993","indexId":"70209677","displayToPublicDate":"2020-04-19T09:46:12","publicationYear":"2020","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":"A critical assessment of human-impact indices based on anthropogenic pollen indicators","docAbstract":"Anthropogenic pollen indicators in pollen records are an established tool for reconstructing the history of human impacts on vegetation and landscapes. They are also used to disentangle the influence of human activities and climatic variability on ecosystems. The comprehensive anthropogenic pollen-indicator approach developed by Behre (1981) has been widely used, including beyond its original geographical scope of Central and Western Europe. Uncritical adoption of this approach for other areas is risky because adventives (plants introduced with agriculture) in Central Europe can be apophytes (native plants favoured by human disturbances) in other regions. This problem can be addressed by identifying region-specific, anthropogenic-indicator pollen types and/or developing region-specific, human-impact indices from pollen assemblages. However, understanding of regional variation in the timing and intensity of human impacts is limited by the lack of standardization, validation and intercomparison of such regional approaches. Here we review the most common European anthropogenic pollen-indicator approaches to assess their performance at six sites spanning a continental gradient over the boreal, temperate and Mediterranean biomes. Specifically, we evaluate the human-indicator approaches by using independent archaeological evidence and models. We present new insights into how these methodologies can assist in the interpretation of pollen records as well as into how a careful selection of pollen types and/or indices according to the specific geographical scope of each study is key to obtain meaningful reconstructions of anthropogenic activity through time. The evaluated approaches generally perform better in the regions for which they were developed. However, we find marked differences in their capacity to identify human impact, while some approaches do not perform well even in the regions for which they were developed, others might be used, with due caution, outside their original areas or biomes. We conclude that alongside the increasing wealth of pollen datasets a need to develop novel tools may assist numeric human impact reconstructions.","language":"English","publisher":"Elsevier","doi":"10.1016/j.quascirev.2020.106291","collaboration":"","usgsCitation":"Deza-Araujo, M., Morales-Molino, C., Tinner, W., Henne, P., Heitz, C., Pezzatti, G.B., Hafner, A., and Conedera, M., 2020, A critical assessment of human-impact indices based on anthropogenic pollen indicators: Quaternary Science Reviews, v. 236, https://doi.org/10.1016/j.quascirev.2020.106291.","productDescription":"106291, 12 p.","startPage":"","ipdsId":"IP-115699","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":457030,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.quascirev.2020.106291","text":"Publisher Index Page"},{"id":374154,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"236","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Deza-Araujo, Mara 0000-0001-9628-771X","orcid":"https://orcid.org/0000-0001-9628-771X","contributorId":224223,"corporation":false,"usgs":false,"family":"Deza-Araujo","given":"Mara","email":"","affiliations":[{"id":40840,"text":"Swiss Federal Institute for Forest, Snow, and Landscape Research (WSL)","active":true,"usgs":false}],"preferred":false,"id":787479,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Morales-Molino, Cesar 0000-0002-9464-862X","orcid":"https://orcid.org/0000-0002-9464-862X","contributorId":224224,"corporation":false,"usgs":false,"family":"Morales-Molino","given":"Cesar","email":"","affiliations":[{"id":38843,"text":"University of Bern, Switzerland","active":true,"usgs":false}],"preferred":false,"id":787480,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tinner, Willy 0000-0001-7352-0144","orcid":"https://orcid.org/0000-0001-7352-0144","contributorId":169167,"corporation":false,"usgs":false,"family":"Tinner","given":"Willy","email":"","affiliations":[{"id":25430,"text":"University of Bern","active":true,"usgs":false}],"preferred":false,"id":787481,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Henne, Paul D. 0000-0003-1211-5545 phenne@usgs.gov","orcid":"https://orcid.org/0000-0003-1211-5545","contributorId":169166,"corporation":false,"usgs":true,"family":"Henne","given":"Paul D.","email":"phenne@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":787482,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Heitz, Caroline 0000-0001-7188-6775","orcid":"https://orcid.org/0000-0001-7188-6775","contributorId":224225,"corporation":false,"usgs":false,"family":"Heitz","given":"Caroline","email":"","affiliations":[{"id":38843,"text":"University of Bern, Switzerland","active":true,"usgs":false}],"preferred":false,"id":787483,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pezzatti, Gianni B 0000-0003-2564-3435","orcid":"https://orcid.org/0000-0003-2564-3435","contributorId":224226,"corporation":false,"usgs":false,"family":"Pezzatti","given":"Gianni","email":"","middleInitial":"B","affiliations":[{"id":40840,"text":"Swiss Federal Institute for Forest, Snow, and Landscape Research (WSL)","active":true,"usgs":false}],"preferred":false,"id":787484,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hafner, Albert 0000-0003-2159-8569","orcid":"https://orcid.org/0000-0003-2159-8569","contributorId":224227,"corporation":false,"usgs":false,"family":"Hafner","given":"Albert","email":"","affiliations":[{"id":38843,"text":"University of Bern, Switzerland","active":true,"usgs":false}],"preferred":false,"id":787485,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Conedera, Marco 0000-0003-3980-2142","orcid":"https://orcid.org/0000-0003-3980-2142","contributorId":194727,"corporation":false,"usgs":false,"family":"Conedera","given":"Marco","email":"","affiliations":[],"preferred":false,"id":787486,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70209696,"text":"70209696 - 2020 - Inferring surface flow velocities in sediment-laden Alaskan rivers from optical image sequences acquired from a helicopter","interactions":[],"lastModifiedDate":"2020-04-21T16:53:17.461883","indexId":"70209696","displayToPublicDate":"2020-04-18T11:48:25","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Inferring surface flow velocities in sediment-laden Alaskan rivers from optical image sequences acquired from a helicopter","docAbstract":"The remote, inaccessible location of many rivers in Alaska creates a compelling need for remote sensing approaches to streamflow monitoring. Motivated by this objective, we evaluated the potential to infer flow velocities from optical image sequences acquired from a helicopter deployed above two large, sediment-laden rivers. Rather than artificial seeding, we used an ensemble correlation particle image velocimetry (PIV) algorithm to track the movement of boil vortices that upwell suspended sediment and produce a visible contrast at the water surface. This study introduced a general, modular workflow for image preparation (stabilization and geo-referencing), preprocessing (filtering and contrast enhancement), analysis (PIV), and postprocessing (scaling PIV output and assessing accuracy via comparison to field measurements). Applying this method to images acquired with a digital mapping camera and an inexpensive video camera highlighted the importance of image enhancement and the need to resample the data to an appropriate, coarser pixel size and a lower frame rate. We also developed a Parameter Optimization for PIV (POP) framework to guide selection of the interrogation area (IA) and frame rate for a particular application. POP results indicated that the performance of the PIV algorithm was highly robust and that relatively large IAs (64–320 pixels) and modest frame rates (0.5–2 Hz) yielded strong agreement (